Miracle-dz/UniFAR-FTUs huggingface dataset:
Robotic Etching: The creation of digital etching using robotics

1. Introduction This paper describes an investigation of a robot used within the traditional etching printmaking process. The use of a robot has been studied in two ways. First, for the novel affordances an autonomous robot provides within printmaking, and secondly, as a study of how robotic technologies can be integrated within a traditional process. In this second aspect, the first author has worked as mediator, facilitator and researcher between the robot as tool and with Ian Chamberlain as artist. This paper therefore reflects on both the accessibility and utility of robotics within art as a new tool, and upon the potential expressive capabilities of a robot within the etching printmaking field. Technological advances, particularly within the fields of AI and robotics in recent years, has led to new possibilities when it comes to producing artwork. New robotic machines capable of both creating and physically rendering artefacts have opened new areas of investigation around machine creativity and human/robotic collaboration. This paper looks at the potentials afforded by robots when applied to traditional printing techniques such as etching. Through experiments using an adapted 3D printing robot, this study explores the possibilities of creating works where a traditional etching needle is used by a robot. The traditional etching medium has a reliance on physical mark making, which is captured on the print plate. Therefore, purely digital approaches, i.e, predominantly screen based digital media, do not sufficiently emulate the nuances of tactile interaction. However, a robot provides a unique opportunity to transform purely digital information through the physical mechanisms of a robot. Therefore, a robot potentially has some capability for nuanced mark making. However, it is not clear how accessible this is, how well it can be used, how valuable it is or how effective it is in complement to human expression.
Moving Object Tracking Using CAMSHIFT and SURF Algorithm

Penjejakan objek bergerak (moving object tracking) sebagai sebuah permasalahan yang berperan penting dalam bidang computer vision dan secara luas dapat diterapkan dalam banyak aplikasi dunia nyata seperti pengawasan otomatis, human pose estimation, navigasi kendaraan, pemantauan lalu lintas, dan robot vision. Moving object tracking membutuhkan metode yang memiliki akurasi dan ketahanan yang baik terhadap perubahan yang terjadi pada objek. Penelitian ini membangun sebuah aplikasi untuk membandingkan Algoritma Camshift (Continuosly Adaptive Mean-Shift) dan Algoritma SURF (Speeded Up Robust Feature). Aplikasi dapat melakukan penjejakan dengan menggunakan kedua metode sekaligus. Pengujian dilakukan dengan lima kondisi pergerakan objek yang berbeda pada tiga warna latar belakang berbeda untuk membandingkan waktu komputasi dan akurasi dari kedua metode. Hasil pengujian menunjukkan bahwa akurasi Camshift lebih baik dibanding Surf, sementara untuk waktu komputasi Surf mengungguli Camshift.
Massive RDF Query Processing Efficiently in Spark Environment Based on Semantic Connection Set

Resource Description Framework(RDF) is a data representation format of the Semantic Web, and its data volume is growing rapidly. Cloud-based systems provide a rich platform for managing RDF data. However, the distributed environment has performance challenges when it is processing with RDF queries that contain multiple join operations, such as network reshuffle, memory overhead. To get over these challenges, this paper proposed a spark-based RDF query architecture, which is based on Semantic Connection Set (SCS). First of all, this spark-based query architecture adopts the mechanism of re-partitioning class data based on vertical partitioning, which can reduce memory overhead and fast index data. Secondly, a method for generating query plans based on semantic connection sets is proposed in this paper. In addition, statistics and broadcast variable optimization strategies are used to reduce shuffling and data communication costs. The experiment of this paper is based on the latest SPARQLGX on the spark platform RDF system, two synthetic benchmarks are used to evaluate the query. The experiment result illustrates that the proposed approach in this paper is more efficient in data search than SPARQLGX.
Towards a Swedish Medical Treebank

Treebanks are linguistically annotated corpora with some previously established scheme of grammatical analysis. In any domain and in the (bio-) medical field in particular, such resources constitute a fundamental piece of knowledge for empirically-based, data-driven language processing, human language technologies and linguistic research and have attracted an increased interest during recent years. The interest for treebanks in biomedicine is guided by the fact that information extraction and (bio-) text mining research is shifting focus from the extraction and annotation of named entities to the extraction and annotation of relations and interactions between entities. This is usually associated by the extraction of verbal – alias predicate-argument – structures (cf. Kulick et al. [1], Tateisi et al. [2]). Semantic relations (e.g. between entities) and role extraction and labelling (e.g. agent, object) constitute a considerable challenge for automatic tools, although recent evaluation competitions such as the PASBio (Wattarujeekrit et al., [3]) and the BioCreAtIvE (Hirschman et al., [4]) revealed that some systems could present significant progress in performance in this area. In this paper, we present our current activities towards the compilation and the multi-layered annotation of a domain-dependent corpus for Swedish in the area of medicine. The focus of the paper is based on the description of the constituent structure and functionally oriented annotation of the corpus. Moreover, the annotation scheme adopted, which incorporates three main layers of linguistic processing, lexical analysis, shallow semantic analysis and syntactic processing, will be exemplified. For the syntactic analysis we use a cascaded finite-state parser, aware of the shallow semantic annotations produced. The result of this analysis, including syntactic parsing and shallow semantic analysis, is transformed into the TIGER-XML interchange format ([5]). Our goal is to produce a large, rich in annotations, medical treebank suitable for both corpus-based grammar learning systems, for semantic relation extraction and for linguistic exploration of theoretical nature. Motivation for this work is given in Section 2. Background work in the area of biomedical syntactic analysis and treebanking is presented in Section 3. Section 4 gives a brief description of the corpus used in this work, while Section 5 deals with the pre-processing steps applied into a sample of the corpus. Section 6 presents evaluation results based on this sample, while Section 7 summarizes the paper and proposes directions for future work.
Distributed Nonlinear Integer Optimization for

We present a novel approach for joint optical net- work provisioning and Internet protocol (IP) traffic engineering, in which the IP and optical networks collaboratively optimize a combined objective of network performance and lightpath provi- sioning cost. We develop a framework for distributed multilayer optimization. Our framework is built upon the IP-over-optical (IPO) overlay model, where each network domain has a limited view of the other. Our formulation allows the two domains to communicate and coordinate their decisions through minimal information exchange. Our solution is based on a novel appli- cation of Generalized Bender's Decomposition, which divides a difficult global optimization problem into tractable subproblems, each solved by a different domain. The procedure is iterative and converges to the global optimum. We present case studies to demonstrate the efficiency and applicability of our approach in various networking scenarios. Our work builds a foundation for "multilayer" grooming, which extends traditional grooming in the optical domain to include data networks. The data networks are active participants in the grooming process with intelligent homing of data traffic to optical gateways. Index Terms—Capacity expansion, cooperative internet- working, data-optical network, distributed control, generalized multiprotocol label switching (GMPLS), nonlinear integer opti- mization, optical grooming, traffic engineering, wavelength.
Electric Vehicle Fast Charging Needs in Cities and along Corridors

Fast charging is seen as a means to facilitate long-distance driving for electric vehicles (EVs). As a result, roll-out planning generally takes a corridor approach. However, with higher penetration of electric vehicles in urban areas, cities contemplate whether inner-city fast chargers can be an alternative for the growing amount of slow public chargers. For this purpose, more knowledge is required in motives and preferences of users and actual usage patterns of fast chargers. Similarly, with increasing charging speeds of fast chargers and different modes (taxi, car sharing) also switching to electric vehicles, the effect of charging speed should be evaluated as well as preferences amongst different user groups. This research investigates the different intentions and motivations of EV drivers at fast charging stations to see how charging behaviour at such stations differs using both data analysis from charging stations as a survey among EV drivers. Additionally, it estimates the willingness of EV drivers to use fast charging as a substitute for on-street home charging given higher charging speeds. The paper concludes that limited charging speeds imply that EV drivers prefer parking and charging over fast charging but this could change if battery developments allow higher charging speeds.
Video coding for low bit rate communications (H.263)

The H.263 Recommendation specifies a coded representation that can be used for compressing the moving picture components of audio-visual services at low bit rates. Detailed specifications of the first generation of this codec under the test model (TM) to verify the performance and compliance of this codec were finalised in 1995. The basic configuration of the video source algorithm in this codec is based on ITU-T Recommendation H.261, which is a hybrid of interpicture prediction to utilise temporal redundancy and transform coding of the residual signal to reduce spatial redundancy. However, during the course of the development of H.261 and the subsequent advances on video coding in MPEG-1 and MPEG-2 video codecs, substantial experience was gained, which has been exploited to make H.263 an efficient encoder. In this chapter those parts of the H.263 standard that make this codec more efficient than its predecessors will be explained.
Efficient Predictive Structure Algorithm for MV-HEVC

Efficient prediction structure for multi-view high efficiency video coding (MV-HEVC) is a key factor in determining performance such as compression efficiency, random access, and scalability. At present, MV-HEVC reference software provided by the international standard organization JCT-3V adopts a fixed inter-view prediction structure, which makes it difficult to adaptively adjust the prediction structure. Therefore, this paper proposes a novel predictive structure algorithm based on multi-objective optimization for MV-HEVC. This paper, the design of MV-HEVC prediction structure is regarded as a multi-objective optimization problem. The idea of this paper is to first determine the position of the I-view based on the similarity analysis between the viewpoints. Then, considering the coding efficiency and the random access of the user, the multi-objective optimization function is established to solve the coded B-viewpoint between the I-viewpoint and the P-viewpoint. In the end, this paper flexibly adjusts the inter-view prediction structure according to the prediction structure coding parameters to improve the coding performance. Experiments show that compared with MV-HEVC, the proposed algorithm not only improves the coding efficiency but also has better the random access performance.
Linguistic and computational modeling in language science

historical perspectives. When practiced as a science, linguistics is characterized by the tension between the two methodological dispositions of rationalism and empiricism. At any point in time in the history of linguistics, one is more dominant than the other. In the last two decades, we have been experiencing a new wave of empiricism in linguistic fields as diverse as psycholinguistics (e.g., Chater et al., 2015), language typology (e.g., Piantidosi and Gibson, 2014), language change (e.g., Bybee, 2010) and language variation (e.g., Bresnan and Ford, 2010). Consequently, the practices of modeling are being renegotiated in different linguistic communities, readdressing some fundamental methodological questions such as: How to cast a research question into an appropriate study design? How to obtain evidence (data) for a hypothesis (e.g., experiment vs. corpus)? How to process the data? How to evaluate a hypothesis in the light of the data obtained? This new empiricism is characterized by an interest in language use in context accompanied by a commitment to computational modeling, which is probably most developed in psycholinguistics, giving rise to the field of “computational psycholinguistics” (cf. Crocker, 2010), but recently getting stronger also in corpus linguistics. The predominant domain of corpus linguistics is language variation, aiming at statements on relative differences/similarities between linguistic varieties (time periods, registers, genres). Corpus analysis is thus comparative by nature; technically, this involves comparing probability distributions of (sets of) linguistic features (e.g., the relative frequency of passive vs. active voice in narrative vs. expository genres) and assessing whether they are significantly different or not. Here, descriptive statistical techniques come into play but also language modeling and machine learning methods (e.g., clustering, latent semantic analysis, or Bayesian modeling). Similarly, corpus processing—that is, preparing text material for analysis—relies on computational models, for example, for annotation. What is important to note here is that processing and analysis are broken up into different steps, each using a different computational micro-model that takes care of a specific task (e.g., labeling linguistic units in annotation) and consists of a descriptive component (set of allowed labels) and an analytic or algorithmic component (procedure by which labels are assigned). Linguistic and computational modeling in language science
Predicting the Outcome of Deliberative Democracy: A Research Proposal

As liberal states across the world face a decline in political participation by citizens, deliberative democracy is a promising solution for the public’s decreasing confidence and apathy towards the democratic process. Deliberative dialogue is method of public interaction that is fundamental to the concept of deliberative democracy. The ability to identify and predict consensus in the dialogues could bring greater accessibility and transparency to the face-to-face participatory process. The paper sets out a research plan for the first steps at automatically identifying and predicting consensus in a corpus of German language debates on hydraulic fracking. It proposes the use of a unique combination of lexical, sentiment, durational and further ‘derivative’ features of adjacency pairs to train traditional classification models. In addition to this, the use of deep learning techniques to improve the accuracy of the classification and prediction tasks is also discussed. Preliminary results at the classification of utterances are also presented, with an F1 between 0.61 and 0.64 demonstrating that the task of recognising agreement is demanding but possible.
A combined supervised and unsupervised approach to classification of multitemporal remote sensing images

A combined supervised and unsupervised approach to classification of multitemporal remote sensing images is presented. Such an approach performs the automatic classification of a remote sensing image for which training data are not available by exploiting the information derived from an image acquired in the same area at a previous time. Experiments, carried out on a multitemporal data set, point out the effectiveness of the proposed method.
Grammar Efficiency of Parts-of-Speech Systems

Abstract Vulanović’s formula for calculating grammar efficiency is applied to 17 theoretically possible parts-of-speech systems. It is demonstrated that the formula represents grammar efficiency of parts-of-speech systems more adequately than the methods used in the theory of formal languages. Moreover, it is shown that the correlation between parts-of-speech system types and their grammar efficiency can be modeled by the 3-dimensional generalization of the sigmoid
Odometry from Planar landmarks

—This paper presents a new perception odometry approach using extracted stationary planar features to resolve 5 degrees of freedom of the robot motion. The approach exploits the geometrical properties of the extracted features to determine the transformation of the moving robot, which has perceived these landmarks. This way of localizing can help several applications in indoors and outdoors such as urban canyons, with plenty of planar features. The paper presents the concept and the algorithm, and validates them using a simulated scenario.
Inverted-F antenna with parasitic elements for TETRA handset

The paper presents the VSWR (voltage standing wave ratio) performance and radiation pattern for a low-profile IFA (inverted-F antenna) matched at 450 MHz. It is known that when the IFA height from a conducting ground plane is approximately one-tenth of the wavelength, the frequency bandwidth for a VSWR=2 criterion is approximately 8%. Nakano et al. (1998) proposed two L-figured parasitic elements added to the IFA to widen the VSWR bandwidth. It should be a large value for handset systems if this criterion is applied to structures working at lower frequencies (450 MHz) such as TETRA. A design of IFA is presented with different lengths for parasitic elements to generate two resonance frequencies near the central frequency of design. Antenna characteristics; mounted on the handset, are calculated using the method of moments (MoM) with wire grid models using numerical electromagnetic code NEC2. The mean effective gain (MEG) for different statistical distribution models of incident waves are presented too.
A POWER-AWARE CACHE AND REGISTER FILE DESIGN SPACE EXPLORATION

In the near future, embedded processors need to support more network applications, computation-intensive packet-processing tasks will become heavier, and new performance bottlenecks will be introduced in the embedded system designs. Since memory access delay and also the number of processor registers significantly affect processor performance, cache and register file are two major parts in designing embedded processor architectures. Increasing the sizes of cache and register file leads to the performance improvement in packetprocessing tasks in high traffic networks with too many packets, but the increased area, power consumption, and memory access delay are the overheads of these techniques. Therefore, implementing these components in the optimum size is of great interest in the design of embedded processors. In this regard, this paper explores the effect of cache and register file size on the performance of processors while considering power consumption in calculating the optimum size of these components for embedded applications. The results show that although having bigger caches and register files helps with the performance improvement in embedded processors, increasing the size of these parameters beyond the threshold level makes the performance improvement saturated and finally decreased. Furthermore, a major part of the power of embedded processors is consumed in the memory.
Accelerating Deep Learning Inference via Freezing

Over the last few years, Deep Neural Networks (DNNs) have become ubiquitous owing to their high accuracy on real-world tasks. However, this increase in accuracy comes at the cost of computationally expensive models leading to higher prediction latencies. Prior efforts to reduce this latency such as quantization, model distillation, and any-time prediction models typically trade-off accuracy for performance. In this work, we observe that caching intermediate layer outputs can help us avoid running all the layers of a DNN for a sizeable fraction of inference requests. We find that this can potentially reduce the number of effective layers by half for 91.58% of CIFAR-10 requests run on ResNet-18. We present Freeze Inference, a system that introduces approximate caching at each intermediate layer and we discuss techniques to reduce the cache size and improve the cache hit rate. Finally, we discuss some of the open research challenges in realizing such a design.
INTELLIGENT CATASTROPHE SUPERVISION SYSTEM

Advancement in the field of science and technology is just reaching the far ends of human thought and imagination hence revolutionizing the importance of terrestrial and maritime robotic systems in disaster relief, search and rescue (SAR) and salvage operations. According to the surveys it has been confirmed that for the first time this robots were deployed to explore the remains below the malformed twin towers of the World Trade Centre in New York after the September 2001 terrorist attacks. With the passage of time the impact of flood hurricanes and earthquakes are increasing so the call for robots for all phases of a catastrophic conditions, from deterrence to reaction and revival, will increase as well. It has been found that robots can even play a crucial role in disaster relief by inspecting critical underwater infrastructure and mapping damage. Through this paper an extra emphasis is laid down on the positive going graph of robots that enlightens the importance of application of AI on robots in case of disaster management.
Dynamic control of motion detection by an active, mobile observer

The authors consider the problem of visual motion detection by a moving observer assuming that the observer is active in the acquisition of images. An exploratory activity paradigm is presented that requires small shifts in the location of the focus of expansion (FOE) of the observer motion. A theoretical analysis is done based on geometric considerations to establish conditions that are necessary and sufficient to guarantee motion detection at a point. The detection problem is posed in terms of locally computable image quantities that make it implementable in real time, and free the user from making any global assumptions about the scene that may fail in the real world. This provides a way of dynamically controlling the motion detection strategy to best suit the current goal or purpose of the overall system. <<ETX>>
Grassmannian initialization: Neural network initialization using sub-space packing

We recently observed that convolutional filters initialized farthest apart from each other using offthe-shelf pre-computed Grassmannian subspace packing codebooks performed surprisingly well across many datasets. Through this short paper, we’d like to disseminate some initial results in this regard in the hope that we stimulate the curiosity of the deep-learning community towards considering classical Grassmannian subspace packing results as a source of new ideas for more efficient initialization strategies.