Miracle-dz/UniFAR-FTUs huggingface dataset:
Delft University of Technology Conceptual testing of visual HMIs for merging of trucks

Merging sections are challenging for drivers of heavy goods vehicles. Visual support for merging was evaluated in a simulator. Experiment 1 tested HMIs that provided participants (n = 5) driving on the on-ramp with a top view or various forms of speed advice for accelerating behind or in front of a truck platoon on the freeway. Experiment 2 tested HMIs that provided drivers (n = 18) on the acceleration lane with a top view complemented with speed and gap advice for finding a gap to merge in. Experiment 1 showed that speed advice yielded less unnecessary braking compared to unsupported driving. In Experiment 2, speed advice yielded low satisfaction ratings. Our results highlight the potential of visual support and stress the importance of not visually overloading
PHOTOGRAMMETRIC STUDY OF THE QUBBET EL-HAWA COPTIC CHURCH
(ASWAN, EGYPT)

Abstract. This paper presents the method applied and the results obtained in the photogrammetric study of the Qubbet el-Hawa Coptic Church. After several archaeological interventions, the ruins of this church were completely uncovered of sand. Therefore, a geometric documentation is justified in prevision of possible damages or deterioration that could affect it, considering the weakened status of several structures. In this context, we selected close-range photogrammetry because of the site conditions which include several vertical walls and occlusions that complicated the use of other terrestrial methods. In addition, a real texture of the object was demanded in order to obtain a complete documentation of the church. In this sense, some decorations and wall paintings were still visible causing this requisite of documentation based on real textures. However, the use of terrestrial photogrammetry was complicated due to the presence of occlusions, and in addition the use of Remotely Piloted Aircraft Systems (RPAS) was not allowed. So a method using a mast of about 4 to 6 metres was considered in order to lift the camera, avoiding these obstacles and reducing the amount of photographs needed. Applying this method, a great set of photographs was acquired and the scene covered with convergent images. The results of the photogrammetric processes were several metric products such as a 3D model with real textures, a ground Digital Elevation Model (DEM), several orthoimages, sections, etc. In addition a preliminary virtual reconstruction of the church has been developed based on these results. In conclusion, we have obtained a complete and reliable geometric documentation of the site and other derived products that can be used for other purposes. The method used has demonstrated its efficiency in these types of studies.
A Solution Space Analysis for First-Hop IP Source Address Validation

The IETF working group on Source Address Validation Improvements, SAVI, is chartered to design methods for IP source address validation that complement ingress filtering with finer-grained protection. This document summarizes the discussion in the SAVI working group and design-related conclusions. The purpose of this is two-fold: First, to guide the design process in the working group with written documentation of decisions and their rationale. Second, to provide a measure for assessing the IP source address validation methods that the working group will eventually deliver.
Intelligent Control Strategy for Transient Response of a Variable Geometry Turbocharger System Based on Deep Reinforcement Learning

Deep reinforcement learning (DRL) is an area of machine learning that combines a deep learning approach and reinforcement learning (RL). However, there seem to be few studies that analyze the latest DRL algorithms on real-world powertrain control problems. Meanwhile, the boost control of a variable geometry turbocharger (VGT)-equipped diesel engine is difficult mainly due to its strong coupling with an exhaust gas recirculation (EGR) system and large lag, resulting from time delay and hysteresis between the input and output dynamics of the engine’s gas exchange system. In this context, one of the latest model-free DRL algorithms, the deep deterministic policy gradient (DDPG) algorithm, was built in this paper to develop and finally form a strategy to track the target boost pressure under transient driving cycles. Using a fine-tuned proportion integration differentiation (PID) controller as a benchmark, the results show that the control performance based on the proposed DDPG algorithm can achieve a good transient control performance from scratch by autonomously learning the interaction with the environment, without relying on model supervision or complete environment models. In addition, the proposed strategy is able to adapt to the changing environment and hardware aging over time by adaptively tuning the algorithm in a self-learning manner on-line, making it attractive to real plant control problems whose system consistency may not be strictly guaranteed and whose environment may change over time.
Overview of the 1999 Survey of Active Duty Personnel

Abstract : This report provides an overview of results obtained from the survey of active-duty members. Chapter 1 provides background information on the history of the survey and describes the remainder of the report. Chapter 2, Survey methodology, provides background on survey administration, analytic procedures used in the report, and the presentation of results. Each subsequent chapter focuses on a different survey topic with findings presented for DoD and for subgroups defined by: Service, paygrade, gender, race/ethnicity, family type, and spouse employment status. Chapter, Satisfaction with Military Life, focuses on member satisfaction with military life in general and with various specific components of military life. Chapter 4, Retention, discusses members' stated intent to remain in the military, their significant others' support for continued military service, and actions taken by members to leave the military. Chapter 5, Financial Position of Member, examines items such as household income, personal debt and savings, financial support received from government programs and any financial problems reported by members. Chapter 6, Personnel Tempo, discusses the time commitments required of members, including time away from home in the preceding and upcoming 12 months (including reasons for being away from home), and workload. Finally, Chapter 7, Quality of Life Programs, focuses on members' use of quality of life, education, and childcare programs and examines member attitudes about health care for themselves and their family members. The population of interest for the 1999 Survey of Active Duty Personnel consisted of all Army, Navy, Marine Corps, Air Force, and Coast Guard active-duty members, below the rank of admiral or general, with at least 6 months of active-duty service when surveys were first mailed. Reservists on active-duty assignments for at least 6 months were also eligible.
Hierarchical Multi-Task Network For Race, Gender and Facial Attractiveness Recognition

Deep learning has powered many face related tasks and shown state-of-the-art performance. However, existing deep models are often trained separately for different problems, which results in heavy computational burden. To address this problem, we propose a novel multi-task network with fully convolutional architecture–Hierarchical Multi-task Network (HMT-Net), that simultaneously recognizes a person’s gender, race and facial attractiveness from a given portrait image. Aiming to improve the robustness to outliers in facial beauty prediction task, a novel loss is introduced into HMTNet. Compared to existing deep approaches, the proposed HMTNet achieves state-of-the-art performance on several datasets, and it can learn more discriminative feature representation through joint training and feature aggregation. Extensive experiments evidence the effectiveness of HMTNet.
DeFe: indoor localization based on channel state information feature using deep learning

With the development of mobile devices, more and more location-based services (LBS) on the devices are needed and fingerprint indoor localization has become one most important technique because of its low cost and high accuracy. In this paper, we use the fingerprinting method which based on Channel State Information (CSI) for indoor localization. Furthermore, we extract the raw phase information from the multiple antennas and multiple sub-carriers through the IEEE 802.11n network interface card (NIC 5300) on several special models. Then we extract the required phase information and introduce two methods of mathematical statistics to analyse the feature of CSI signals. We replace the processed phase information with the signal features obtained from the analysis. For the offline stage, we employ a deep network with three hidden layers to train the signal features data, and use weights to represent fingerprints. Introducing a greedy learning algorithm to train the weights layer-by-layer to reduce the computational complexity, and the sub-network between two continuous layers forms a restricted Boltzmann machine (RBM). For the online location estimation, we use a probabilistic method based on the radial basis function (RBF). The neural network method we used this time is tested under two different scenarios, and different data are compared. The final conclusion is that the new method we combined is superior to the previous.
A Meta-Learning Framework for Learning Multi-User Preferences in QoE Optimization of DASH

Dynamic adaptive video streaming over hypertext transfer protocol (DASH) plays a key role in video transmission over the Internet. The conventional DASH adaptation approaches mainly focus on optimizing the overall quality of experience (QoE) for all client sides, neglecting the QoE diversity of different users. In this paper, we propose a meta-learning framework for multi-user preferences (MLMP) as a new DASH adaptation approach, which is able to optimize the diverse QoE of different users. Specifically, we first design a subjective experiment to analyze the difference of QoE preferences across users, in which QoE refers to the metrics of visual quality, fluctuation, and rebuffering events. Based on our findings, we formulate the QoE optimization of multi-user preferences as a multi-task deep reinforcement learning (DRL) problem. In our formulation, the QoE preference of each user is modeled in the overall QoE calculation via assigning the weights to the three QoE metrics. Then, the MLMP framework is developed to solve the proposed multi-task DRL problem, such that the preferences regarding visual quality, fluctuation, and rebuffering events can be optimized for different users in DASH adaptation. Finally, the simulation results show that the proposed approach outperforms state-of-the-art DASH adaptation approaches in satisfying the different users’ QoE preferences regarding visual quality, fluctuation, and rebuffering events.
Advanced CNN Based Motion Compensation Fractional Interpolation

Fractional-sample precision motion compensation has been widely adopted in a series of video coding standards to further improve compression efficiency. Usually, signal decomposition based interpolation filters are used to generate fractional samples from integer pixels. However, the coefficients of these finite impluse response filters may not be suitable for varied video contents and coding conditions because of the assumption when designing these filters. In this paper, we regard the fractional interpolation process as an image generation task, which utilizes the real interger position samples at the reference block to predict and generate fractional samples that are much closer to current coding block. We use the con-volutional neural netwok (CNN) as the generator. Moreover, to make the best of CNN’s powerful nonlinear learning ability, instead of inputting the reference block directly, we separately input the corresponding prediction and residual parts of reference block. The proposed dual-input CNN-based interpolation scheme has been incorporated into the HEVC framework and experimental results demonstrate our approach achieves average 0.9% bitrate reduction.
Review of Big Data and Processing Frameworks for Disaster Response Applications

Natural hazards result in devastating losses in human life, environmental assets and personal, and regional and national economies. The availability of different big data such as satellite imageries, Global Positioning System (GPS) traces, mobile Call Detail Records (CDRs), social media posts, etc., in conjunction with advances in data analytic techniques (e.g., data mining and big data processing, machine learning and deep learning) can facilitate the extraction of geospatial information that is critical for rapid and effective disaster response. However, disaster response systems development usually requires the integration of data from different sources (streaming data sources and data sources at rest) with different characteristics and types, which consequently have different processing needs. Deciding which processing framework to use for a specific big data to perform a given task is usually a challenge for researchers from the disaster management field. Therefore, this paper contributes in four aspects. Firstly, potential big data sources are described and characterized. Secondly, the big data processing frameworks are characterized and grouped based on the sources of data they handle. Then, a short description of each big data processing framework is provided and a comparison of processing frameworks in each group is carried out considering the main aspects such as computing cluster architecture, data flow, data processing model, fault-tolerance, scalability, latency, back-pressure mechanism, programming languages, and support for machine learning libraries, which are related to specific processing needs. Finally, a link between big data and processing frameworks is established, based on the processing provisioning for essential tasks in the response phase of disaster management.
Measuring behavioural and neural responses to fluctuations in real-world predictability

A crucial component of brain function is to predict what will happen next. Although prediction is fundamental to brain function, we often study prediction of lowdimensional abstract tasks, rather than real-world events. We developed a novel behavioural approach to measure the dynamics of “real-world” predictability using audiovisual movies and natural language processing. Participants were shown an 11-minute movie, where viewing was occasionally interrupted by requests to generate sentences predicting what would happen next. These written predictions were converted into sentence embeddings using the Universal Sentence Encoder. Using these embedding vectors, we generated a timecourse of “situation-level” predictability during movie watching, revealing periods associated with homogeneous (high-predictability) and heterogeneous predictions (low-predictability) across-participants. We then regressed this timecourse of predictability on the fMRI data of a separate group of participants who watched the same movie, uninterrupted, in the scanner. During periods of high predictability, we observed higher activity in regions of the default mode network, while during periods of low predictability we observed higher activity in sensory cortices, consistent with internalexternal models of cortical organization. Overall, we demonstrate the utility of natural language processing in quantifying fluctuations in real-world predictability.
Question Answering for Privacy Policies: Combining Computational and Legal Perspectives

Privacy policies are long and complex documents that are difficult for users to read and understand. Yet, they have legal effects on how user data can be collected, managed and used. Ideally, we would like to empower users to inform themselves about the issues that matter to them, and enable them to selectively explore these issues. We present PrivacyQA, a corpus consisting of 1750 questions about the privacy policies of mobile applications, and over 3500 expert annotations of relevant answers. We observe that a strong neural baseline underperforms human performance by almost 0.3 F1 on PrivacyQA, suggesting considerable room for improvement for future systems. Further, we use this dataset to categorically identify challenges to question answerability, with domain-general implications for any question answering system. The PrivacyQA corpus offers a challenging corpus for question answering, with genuine real world utility.
On the Security of SDN: A Completed Secure and Scalable Framework Using the Software-Defined Perimeter

The widespread adoption and evolution of Software Defined Networking (SDN) have enabled the service providers to successfully simplify network management. Along with the traffic explosion, there is decreasing CAPEX and OPEX as well as an increase in the average revenue per user. However, this wide adoption of SDNs is posing real challenges and concerns in terms of security aspects. The main challenges are how to provide proper authentication, access control, data privacy, and data integrity among others for the API-driven orchestration of network routing. Herein, the Software Defined Perimeter (SDP) is proposed as a framework to provide an orchestration of connections. The expectation is a framework that restricts network access and connections between objects on the SDN-enabled network infrastructures. There are several potential benefits as a result of the integration between SDP systems and SDNs. In particular, it provides a completely scalable and managed security solution. Consequently, it leads to flexible deployment that can be tailored to fit the need of any generic network security perimeter. The proposed Integrated frameworks are examined through virtualized network testbeds. The testing results demonstrate that the proposed framework is malleable to both port scanning (PS) attack and Denial of Service (DoS) bandwidth attack. In addition, it clarifies some interesting potential integration points between the SDP systems and SDNs to further research in this area.
In Search of Disruptive Ideas - Outlier Detection Techniques in Crowdsourcing Innovation Platforms

The key challenge for data science in open innovation web systems is to find best ideas among thousands of community submissions. To date, this has been done with metrics reflecting enterprise needs or community preferences. This article proposes to look in a different direction: inspired by theoretical studies on disruptive innovation, we frame the problem of valuable ideas as those rarely taken up by masses or organisations yet having potential to change industries. Our aim is to find technological means for automatic detection of such innovations to aid decision making. Following past findings from business sciences on nature of disruptive innovations, the article presents a comparative study of multiple outlier detection algorithms applied to two real-world datasets containing textual descriptions of ideas for different industries. Obtained results demonstrate capability of outlier detection and show k-NN algorithm with TF-IDF and cosine distance to be the best candidate for the task.
Analysis of Zero-Forcing Vectoring for VDSL2 and Long Reach VDSL Technologies

A Normal approximation for the downstream bit rate per user in modern Fiber-to-the-Cabinet DSL-based access systems such as Very high-speed Digital Subscriber Line type 2 (VDSL2) and Long Reach VDSL (LR-VDSL) is presented in this paper. It accounts for the positions of subscribers along the main cable from the Cabinet, for the Far-end cross-talk (FEXT) fluctuations, for the transmitter power spectra of subscribers and for zero forcing vectoring. A stochastic model relating the FEXT to residual FEXT after zero forcing vectoring pre-coding is also presented. The proposed Normal model is derived by assuming Log-normal statistics of the signal-to-interference plus noise ratio per sub-carrier. Its validity is assessed by computer calculation by superimposing the cumulative distribution functions obtained from exact bit rate calculation with those obtained with the Normal model. Differences of few Mbit/s have been observed for high percentiles thus proving the effectiveness of the proposed model. We applied the model to assess coexistence among different DSL technologies such as VDSL2 and LR-VDSL sharing the same copper cable from the Cabinet and even to evaluate the downstream bit rate for G.fast with zero forcing vectoring.
Identifying enhancer-promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism

MOTIVATION Identification of enhancer-promoter interactions (EPIs) is of great significance to human development. However, experimental methods to identify EPIs cost too much in terms of time, manpower and money. Therefore, more and more research efforts are focused on developing computational methods to solve this problem. Unfortunately, most existing computational methods require a variety of genomic data, which are not always available, especially for a new cell line. Therefore, it limits the large-scale practical application of methods. As an alternative, computational methods using sequences only have great genome-scale application prospects. RESULTS In this article, we propose a new deep learning method, namely EPIVAN, that enables predicting long-range EPIs using only genomic sequences. To explore the key sequential characteristics, we first use pre-trained DNA vectors to encode enhancers and promoters; afterwards, we use one-dimensional convolution and gated recurrent unit to extract local and global features; lastly, attention mechanism is used to boost the contribution of key features, further improving the performance of EPIVAN. Benchmarking comparisons on six cell lines show that EPIVAN performs better than state-of-the-art predictors. Moreover, we build a general model, which has transfer ability and can be used to predict EPIs in various cell lines. AVAILABILITY AND IMPLEMENTATION The source code and data are available at: https://github.com/hzy95/EPIVAN.