Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying Privacy Vulnerability to Socialbot Attacks: An Adaptive Non-Submodular Model

Privacy breaches are one of the biggest concerns on Online Social Networks (OSNs), especially with an introduction of automated attacks by <italic>socialbots</italic>, which can automatically extract victims’ private content by exploiting social behavior to befriend them. The key insight of this attack is that by intelligently sending friend requests to a small subset of users, called the Critical Friending Set (CFS), such a bot can evade current defense mechanisms. We study the vulnerability of OSNs to socialbot attacks. Specifically, we introduce a new optimization problem, Min-Friending, which identifies a minimum CFS to friend in order to obtain at least <inline-formula><tex-math notation="LaTeX">$Q$</tex-math><alternatives><mml:math><mml:mi>Q</mml:mi></mml:math><inline-graphic xlink:href="li-ieq1-2840433.gif"/></alternatives></inline-formula> <italic>benefit</italic>, which quantifies the amount of private information the bot obtains. The two main challenges of this problem are how to cope with incomplete knowledge of network topology and how to model users’ responses to friend requests. In this paper, we show that Min-Friending is inapproximable within a factor of <inline-formula><tex-math notation="LaTeX">$(1-o(1))\ln Q$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:mi>o</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo><mml:mo>)</mml:mo><mml:mo form="prefix">ln</mml:mo><mml:mi>Q</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="li-ieq2-2840433.gif"/></alternatives></inline-formula> and present an adaptive approximation algorithm using adaptive stochastic optimization. The key feature of our solution lies in the adaptive method, where partial network topology is revealed after each successful friend request. Thus the decision of whom to send a friend request to next is made with the outcomes of past decisions taken into account. Traditional tools break down when attempting to place a bound on the performance of this technique with realistic user models. Therefore, we additionally introduce a novel curvature-based technique to construct an approximation ratio of <inline-formula><tex-math notation="LaTeX">$\ln {Q}$</tex-math><alternatives><mml:math><mml:mrow><mml:mo form="prefix">ln</mml:mo><mml:mi>Q</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="li-ieq3-2840433.gif"/></alternatives></inline-formula> for a model of user behavior learned from empirical measurements on Facebook.
Comparison of optimization methods for model predictive control: An application to a compressed air energy storage system

This thesis compares different linear and nonlinear optimization methods for model predictive control of energy storage systems. The methods are applied to a compressed air energy storage system. Model predictive control is used to minimize operational costs covering a given 24-hour air demand using a time-sensitive electricity price as an incentive. The experiments are performed for several scenarios with variations in demand, electricity price, optimization timestep size and forecast quality.
Post-merger Integration - Management of Intercultural Differences between a Japanese Corporation and a German Hidden Champion

The purpose of this paper is to indicate obvious aspects with regard to differences in national and organisational cultures in co-operations between two international companies and to give suggestions how to resolve them. Based on a series of qualitative and quantitative interviews, the following research presents an acquisition from a Bavarian, countryside medium-sized German automation company (PLC company with roughly EUR 30 million Sales and 250 employees) by an international Japanese machinery building corporation (acting in same field of business with a stronger focus on the machinery building, stock listed co-operation with roughly EUR 3 billion sales and 14.000 employees). The article outlines cultural and management integration strategies to ensure successful collaboration for the future to secure the strength of the German medium-sized company. The findings show that the personal readiness to understand, adapt and tolerate the cultural differences helps to facilitate negotiations and though, the process of cultural integration is a soft topic and takes several years, it can and should be planned systematically and precisely integrated by the top management to achieve effective change for the realisation of synergies. The elaborated suggestions are helpful for managers in general, because the often underestimated topic of cultural integration, is at least as important for the valuable outcome of the co-operation as strategic and organisational changes.
A stable graph layout algorithm for processes

Process mining enables organizations to analyze data about their (business) processes. Visualization is key to gaining insight into these processes and the associated data. Process visualization requires a high‐quality graph layout that intuitively represents the semantics of the process. Process analysis additionally requires interactive filtering to explore the process data and process graph. The ideal process visualization therefore provides a high‐quality, intuitive layout and preserves the mental map of the user during the visual exploration. The current industry standard used for process visualization does not satisfy either of these requirements. In this paper, we propose a novel layout algorithm for processes based on the Sugiyama framework. Our approach consists of novel ranking and order constraint algorithms and a novel crossing minimization algorithm. These algorithms make use of the process data to compute stable, high‐quality layouts. In addition, we use phased animation to further improve mental map preservation. Quantitative and qualitative evaluations show that our approach computes layouts of higher quality and preserves the mental map better than the industry standard. Additionally, our approach is substantially faster, especially for graphs with more than 250 edges.
A recent overview of the integration of System Dynamics and Agent-based Modelling and Simulation

Modelling and simulation aim to reproduce the structure and imitate the behavior of real-life systems. For complex dynamic systems, System Dynamics (SD) and Agent-based (AB) modelling are two widely used modelling paradigms that prior to the early 2010’s have traditionally been viewed as mutually exclusive alternatives. This literature review seeks to update the work of Scholl (2001) and Macal, (2010) by providing an overview of attempts to integrate SD and AB over the last ten years. First, the building blocks of both paradigms are presented. Second, their capabilities are contrasted, in order to explore how their integration can yield insights that cannot be generated with one methodology alone. Then, an overview is provided of recent work comparing the outcomes of both paradigms and specifying opportunities for integration. Finally, a critical reflection is presented. The literature review concludes that while paradigm emulation has contributed to expanding the applications of SD, it is the dynamic combination of the two approaches that has become the most promising research line. Integrating SD and AB, and even tools and methods from other disciplines, makes it possible to avoid their individual pitfalls and, hence, to exploit the full potential of their complementary characteristics, so as to provide a more complete representation of complex dynamic systems.
Multi-agent Consensus Problem Based on Observer and Considered Lur’e Nonlinearity in Directed Topology

This paper studies the multi-agent systems consensus problem considering Lur’e-type nonlinearity under directed network. Compared with the conclusions of the existing undirected communication topology, the conclusions obtained have greatly reduced the communication topology requirements and are more general. This paper designs a control protocol based on the observer, which solves the problem that the system state information is unknown. By decomposition of a specific form of the Laplacin matrix, consensus problem is converted into stability problem for low-dimensional systems. In this paper, the method of solving the control protocol is given and proved by the piecewise Lyapunov function. The simulation results show that the designed control protocol can solve the consistency problem.
Analyzing Fine-Grained Resource Utilization for Efficient GPU Workload Allocation

Recently, GPU expands application domains from graphic processing to various kinds of parallel workloads. However, current GPU systems focus on the maximization of each workload’s parallelism through simplified control rather than considering various workload characteristics. This paper classifies the resource usage characteristics of GPU workloads into computing-bound, memory-bound, and dependency-latency-bound, and quantifies the fine-grained bottleneck for efficient workload allocation. For example, we identify the exact bottleneck resources such as single function unit, double function unit, or special function unit even for the same computing-bound workloads. Our analysis implies that workloads can be allocated together if fine-grained bottleneck resources are different even for the same computing-bound workloads, which can eventually contribute to efficient workload allocation in GPU.
Compression of baseband signals in base transceiver systems

A signal compression method and apparatus for a base transceiver system (BTS) in a wireless communication network provides efficient transfer of compressed signal samples over serial data links in the system. For the uplink, an RF unit of the BTS compresses baseband signal samples resulting from analog to digital conversion of a received analog signal followed by digital downconversion. The compressed signal samples are transferred over the serial data link to the baseband processor then decompressed prior to normal signal processing. For the downlink, the baseband processor compresses baseband signal samples and transfers the compressed signal samples to the RF unit. The RF unit decompresses the compressed samples prior to digital upconversion and digital to analog conversion to form an analog signal for transmission over an antenna. Compression and decompression can be incorporated into operations of conventional base stations and distributed antenna systems, including OBSAI or CPRI compliant systems.
A Framework for Cluster-based Rendering and Postprocessing

Despite the ever increasing performance of computer hardware, single machines are not always capable of processing complex tasks in reasonable time, if at all. At the same time, the amount of data to be generated or processed by computer systems increases at a similar rate. Generating images using algorithms from computer graphics is a good example for such scenarios. Because visual analysis of data can greatly benefit from the usage of large, tiled display systems, and techniques to render images become more and more complex in order to generate visually pleasing results, using a single machine is often not sufficient to enable reasonable workflows.In this paper, we introduce a framework for cluster-based rendering and postprocessing. Distributing the generation and postprocessing of images to a cluster of machines allows to speed up these tasks. In particular, we aim at rendering images at high resolutions, e.g. for large display walls. To our knowledge, no other framework for distributed rendering is capable of postprocessing images in a distributed fashion without being limited to frame tiles.We present a software design that provides a simple interface for creating render sessions, enabling arbitrary applications to delegate the generation of images to a cluster. In addition to a simple configuration process, we intend to support platform interoperability. We focus on the design of a flexible and modular architecture in order to make the framework independent of rendering techniques and scheduling algorithms. Thus, these components should be interchangeable to enable a variety of use cases. The same applies to the integration of postprocessing methods.
Motion Path Reconstruction in Indoor Environment Using Commodity Wi-Fi

With the increasing need for indoor localization based application and widely use of Wi-Fi device, Wi-Fi signal is employed to the human positioning of indoor environments due to the absence of global position system. Differing from prior works of position-based scheme and track-based scheme, the path reconstruction for indoor localization proposed in this paper utilizes path segmentation and combination to reconstruct trajectory of human motion. To enhance the universality in different environments and roam easily to new scenarios, our proposed method applies unsupervised learning identification models based on artificial neural network to recognize different benchmark paths. Then, the segments of test motion path will be matched with the most similar benchmark paths, respectively, to reconstruct whole motion path in chronological order. Experimental results of two experiment scenarios indicate that the proposed system could achieve satisfactory accuracy and robustness.
Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation

An extensive list of risks relative to big data frameworks and their use through models of artificial intelligence is provided along with measurements and implementable solutions. Bias, interpretability and ethics are studied in depth, with several interpretations from the point of view of developers, companies and regulators. Reflexions suggest that fragmented frameworks increase the risks of models misspecification, opacity and bias in the result; Domain experts and statisticians need to be involved in the whole process as the business objective must drive each decision from the data extraction step to the final activatable prediction. We propose an holistic and original approach to take into account the risks encountered all along the implementation of systems using artificial intelligence from the choice of the data and the selection of the algorithm, to the decision making.
Evaluating Cultural Transmission in Bronze Age burial rites of Central, Northern and Northwestern Europe using radiocarbon data

European Bronze Age archaeology traditionally focusses on two major dimensions to categorise burials – although there is an immense variability of attendant phenomena within this spectrum – flat graves versus burial mounds and cremation versus inhumation. These traits are an indispensable ingredient for common archaeological narratives of sociocultural interaction and cultural evolution. This article presents a quantitative reconstruction of the general trends in the distribution of Bronze Age burial traditions based on bulk radiocarbon data and employs the resulting time series for the estimation of macro-regional cultural distance. Despite the relatively small amount of input data, the trend reconstruction fits to established archaeological observations for prehistoric Europe. The comparison of cultural and spatial distance leads to the remarkable result of no significant permanent correlation, which indicates that the spread of the relevant aspects of burial traditions cannot be explained with simple diffusion models. Instead, a more complex process of cultural transmission has to be considered.
Commutation Failure Prediction Method Considering Commutation Voltage Distortion and DC Current Variation

Commutation failure (CF) at the inverter side is one of the most common failures in the line-commutated converter-based high voltage direct current (LCC-HVDC) system. Effective prediction of CF helps to formulate and implement protection measures timely. Since abnormal commutation voltage and DC current are the main reasons causing CF, these two factors should be fully considered in CF prediction. In this paper, to consider the effect of commutation voltage distortion, the time-domain response of the commutation voltage Fourier coefficient is deduced based on the first-order circuit response. To consider the effect of DC current variation, the time-domain response of the DC current is derived by superimposing its steady-state component and transient component, respectively. Based on these two predictive value, the extinction angle (EA) during the commutation is calculated according to the commutation voltage-time area, and the criterion based on EA is proposed to predict the occurrence of CF. The simulation results of the test system built in the PSCAD/EMTDC demonstrate the effectiveness and validity of this method.
Study TOUR for Computer Science Students

This paper presents a general framework of an innovative TOUR model with four interrelated elements: Transformation, Outreach, Unification and Reinforcement, enhancing the learning experiences of computer science/computing students via a study tour. We brought our students on an overseas trip as an integral part of an academic course. The immediate goal of the study tour was to gear the students toward the program learning outcomes. Another goal was to take a first step in supporting the TOUR model, to immerse students within these four interrelated important learning and self-improvement elements, and to support each student in becoming a better person. The study tour comprises six core learning activities aligned with the TOUR elements. Evaluations were conducted based on a student survey and self-reflection. The results provide valuable insights into the design of the computer science student study tour, as well as the potential value of the TOUR model.
Pain-awareness multistream convolutional neural network for pain estimation

Abstract. In medical institutions, pain is one of the important clues for patients to transmit their conditions effectively, which makes the estimation of pain status an exceedingly important task. Of late, many methods have been proposed to address this task. However, most of them estimate the pain from entire face images or videos instead of paying more attention to the regions most relevant to pain. We propose a pain-awareness multistream convolutional neural network (CNN) for pain estimation. Specifically, we separate the regions most relevant to the pain expression, and the multistream CNN is used to learn the corresponding pain-awareness features. These features are combined into pain features with adaptive weights to estimate the intensity of pain. Extensive experiments on the publicly available pain database indicate that our multistream CNN-based method has achieved inspiring results compared to the state-of-the-art technologies.
A Teaching Assistant Robot Design Tool Based on Knowledge Chunks Reuse

To reduce the cost to develop service robot applications, we have been developing PRINTEPS, an AI and service robot application development platform for end users. PRINTEPS provides a scenario editor to describe the workflows for robots' actions and human-robot interactions. The scenario editor enables the developer to create workflows while reusing the entire workflows or individual operation processes; however, since end users such as domain experts usually do not know functions and properties of robots, it is difficult to identify and modify the reusable parts to suit the purposes of reuse when reusing the entire workflows. In contrast, it is also difficult to work out different combinations of operation processes when reusing the individual operation processes. To solve these issues, this study proposes a teaching assistant (TA) robot design tool based on knowledge chunks reuse. Two public elementary school teachers created workflows for TA robots using the proposed tool, and each teacher conducted a lesson with TA robots once. Through questionnaires given to the teachers and pupils, the proposed tool and TA robots were evaluated to confirm their usefulness
Sentence Similarity Computation in Question Answering Robot

Computing semantic similarity between sentences or texts is vital in many natural language processing (NLP) tasks such as search, query suggestion, and question answering (QA). Many methods have been developed, based on lexical matching, distributional semantics, etc. However, lexical features, like string matching, fail to capture semantic similarity. In this research, our focus lies on the implementation of distributional representations and how to tune parameters when obtaining representations of words with commonly used word embedding techniques, e.g., Word2Vec and GloVe. We conduct experiments in the setting of Chinese semantic sentence matching tasks on the finance-domain. We examine the goodness of word embedding by both the cosine similarity of semantically similar sentence pairs and semantically dissimilar pairs. Based on our experiments, Word2Vec performs better than GloVe in the sense that Chinese character embedding from Word2Vec yield larger disparity of cosine distances between similar sentence pairs and dissimilar pairs. Also we report the optimal parameters for Word2Vec continuous bag-of-word (CBOW) through our trials, with window size being 6 and embedding dimension being 400, which can be good initial values for other projects.
Estimation and Optimization for Molecular Communications with a Coexistence Constraint

At present, molecular communications has largely been studied in isolation; that is, without the presence of any external biological systems. However, for applications in nanomedicine and advanced manufacturing, this assumption of isolation is unlikely to always hold. A key question is therefore how to design signaling strategies such that the desired information can be communicated, while not disrupting the function of the external biological system. In this paper, we study this problem by establishing a coexistence constraint based on the Kullback-Leibler divergence between the distribution of molecules on the surface of the biological system, with and without the presence of the molecular communication system. We then address the problem of how the transmitting device can estimate these distributions from local observations of its environment. Based on this, we optimize the modulation scheme used by the transmitter subject to the coexistence constraint. Tradeoffs between the coexistence constraint, the probability of error, and the number of samples used to estimate distributions needed to compute the coexistence constraint are then studied numerically.
A Method for Selecting Diabetes Features based on Random Forest

This paper studies the optimal feature subset screening for diabetes according to the health check data based on the random forest algorithm. The paper takes the real physical examination records of the same batch of people in a local health check-up center from 2010 to 2015 as the data source, and evaluates the importance of the features. The preliminary fitting finds that 28 features have an impact on the response results. The AUC performance of the classifier finally selects the optimal feature subset containing 9 characteristic variables in multiple feature subsets, which provides scientific evidence and decision support for medical expert’s prediction intervention, clinical diagnosis, treatment plan determination and medical research on diabetes.