Miracle-dz/UniFAR-FTUs huggingface dataset:
Progressive sparse image sensing using Iterative Methods

Progressive image transmission enables the receivers to reconstruct a transmitted image at various bit rates. Most of the works in this field are based on the conventional Shannon-Nyquist sampling theory. In the present work, progressive image transmission is investigated using sparse recovery of random samples. The sparse recovery methods such as Iterative Method with Adaptive Thresholding (IMAT) and Iterative IKMAX Thresholding (IKMAX) are exploited in this framework since they have the ability for successive reconstruction. The simulation results indicate that the proposed method performs well in progressive recovery. The IKMAX has better final reconstruction than IMAT at the cost of requiring sparse signal with extra information about the sparsity number. However, the IMAT has the ability to recover the compressible signals. Furthermore, two sampling strategies, random sampling and uniform sampling are exploited. The simulations show that IMAT is a better choice for random sampling and IKMAX behaves better than IMAT in the case of uniform sampling.
Finding Modes by Probabilistic Hypergraphs Shifting

In this paper, we develop a novel paradigm, namely hypergraph shift, to find robust graph modes by probabilistic voting strategy, which are semantically sound besides the self-cohesiveness requirement in forming graph modes. Unlike the existing techniques to seek graph modes by shifting vertices based on pair-wise edges (i.e, an edge with $2$ ends), our paradigm is based on shifting high-order edges (hyperedges) to deliver graph modes. Specifically, we convert the problem of seeking graph modes as the problem of seeking maximizers of a novel objective function with the aim to generate good graph modes based on sifting edges in hypergraphs. As a result, the generated graph modes based on dense subhypergraphs may more accurately capture the object semantics besides the self-cohesiveness requirement. We also formally prove that our technique is always convergent. Extensive empirical studies on synthetic and real world data sets are conducted on clustering and graph matching. They demonstrate that our techniques significantly outperform the existing techniques.
A Learning Algorithm for the Appearance-Based Recognition of Complex Objects

This paper addresses the problem of recognizing complex objects in images. The proposed approach is based on a prototype-centered object representation which describes objects as sets of local features. During an evolutionary learning step the model is derived from a set of sample images. The proceeding of the training is measured with regard to the recognition rate as well as the coverage of the training samples. The proposed method is tested by the recognition of 14 classes of cars in highway scenes. A classification rate of 98 percent is achieved.
Valid Debugging for Distributed Shared Memory

Distributed shared memory has been used as an academic research tool for more than a decade. Its wide-spread acceptance and commercial use have been hindered by a lack of support for writing real applications. In particular, DSM debugging tools are almost non-existent. A unified approach to debugging distributed shared memory applications is difficult because of the variations in memory coherence provided by different systems. Furthermore, the behavior of traditional debugging techniques must be modified to cope with relaxed memory consistency semantics. We present a minimal set of debugger commands which must be implemented for useful debugging of DSM programs and explain the notion of valid debugging. The behavior of the commands is defined and proven valid for a specific implementation of sequentially consistent shared memory. Finally we discuss extending this functionality to relaxed consistency systems and applying related techniques for locating programming errors.
Appendix B : The ‘ coefficient of cooperation ’ approach

In the paper the impact of R&D cooperation on prices in experimental duopoly markets is examined. As a theoretical benchmark for the experiment, a two-stage duopoly model with an R&D stage with technological spillovers and a pricing stage is used. For two scenarios of technological spillovers (no versus complete spillovers), a treatment where it is possible to credibly commit to an R&D contract and a baseline treatment without binding contract possibilities, are run. Findings are that, in general, prices are between the subgame perfect Nash and the cooperative level. Further, for both spillover levels prices are higher in periods where R&D contracts are committed to, than in other periods, and to a lesser extent compared to the baseline treatments. JEL codes: C90, L13, O31.
The Influence of a Working Memory Task on Affective Perception of Facial Expressions

In a dual-task paradigm, participants performed a spatial location working memory task and a forced two-choice perceptual decision task (neutral vs. fearful) with gradually morphed emotional faces (neutral ∼ fearful). Task-irrelevant word distractors (negative, neutral, and control) were experimentally manipulated during spatial working memory encoding. We hypothesized that, if affective perception is influenced by concurrent cognitive load using a working memory task, task-irrelevant emotional distractors would bias subsequent perceptual decision-making on ambiguous facial expression. We found that when either neutral or negative emotional words were presented as task-irrelevant working-memory distractors, participants more frequently reported fearful face perception - but only at the higher emotional intensity levels of morphed faces. Also, the affective perception bias due to negative emotional distractors correlated with a decrease in working memory performance. Taken together, our findings suggest that concurrent working memory load by task-irrelevant distractors has an impact on affective perception of facial expressions.
Recent history of trends in vegetation greenness and large-scale ecosystem disturbances in Eurasia

Recent patterns of land cover and vegetation dynamics on the Euasian continent have been linked to changes in the global carbon cycle. Our study was conducted to evaluate patterns in a 19-yr record of global satellite observations of terrestrial vegetation from the Advanced Very High Resolution Radiometer (AVHRR) as a means to characterize major trends in both vegetation egreenness and ecosystem disturbance. The fraction absorbed of photosynthetically active radiation (FPAR) by vegetation canopies worldwide has been computed from the AVHRR at a monthly time interval from 1982 to 2000 and gridded at a spatial resolution of 8 km globally. Unlike previous studies of the AVHRR multiyear time-series of vegetation dynamics, the 8-km spatial resolution makes it possible to compare disturbance events and greenness trends at the same level of spatial detail. Positive trends in FPAR were detected throughout a major greenbelt of central-eastern Europe starting in the mid-1980s. This Eurasian greenbelt extended in a wide swath over the Urals, into the vicinity of Lake Baykal south of the central Siberian plateau, mainly along a latitude belt from 55°. N to 65°N. There was also significantly positive greening in relatively large areas of Great Britain, Italy, Greece, Turkey, the Caucasus and southern India. Nonetheless, a strong downward trend in the FPAR time-series over most of Eurasia was observed by the end of the 1990s. Throughout the 19-yr time period, Eurasia was also impacted by many notable droughts and other disturbance events that could have substantially offset decadal carbon gains attributed to satellite-observed greening. Large-scale ecosystems disturbance events were identified in the FPAR time-series by locating anomalously low values (FPAR-LO) that lasted longer than 12 consecutive months at any 8-km pixel. We find verifiable evidence of numerous disturbance types across Eurasia, including regional patterns of severe droughts, forest fires and insect outbreaks.
Extracting metadata for spatially-aware information retrieval on the internet

This paper presents methods used to extract geospatial information from web pages for use in SPIRIT, a new Geographic Information Retrieval (GIR) system for the web. The resulting geospatial markup tools have been used to annotate around 900,000 web pages taken from a 1TB web crawl, focused on regions in the UK, France, Germany and Switzerland. This paper discusses a versatile geo-parsing tool for extracting spatial metadata based upon the GATE Information Extraction (IE) system, and a simple geo-coding program based on default sense to assign spatial coordinates to extracted locations. A preliminary analysis of markup accuracy for geo-parsing and geo-coding is provided, and an initial statistical and geographical analysis of the SPIRIT collection presented.
Robust feature vector compression algorithm for distributed speech recognition

In this paper we propose an algorithm for efficient compression of feature extracted parameters used in speech recognition. The algorithm provides a compression ratio of roughly 1:10 and causes negligible or no loss in recognition performance. It is also shown to be robust against enviromental noise. Combined with an appropriate framing structure, a complete system is obtained, which can be used for implementing speech recognition applications e.g. in a cellular mobile environment. The system achieves a gross bitrate as low as 4200 bps.
Early career teacher peer support through private groups in social media

ABSTRACT This paper describes the support that Australian early career teachers are accessing through private groups within social media. It presents findings from 22 interviews with teachers who indicated that Facebook was a source of support. Participants describe using private online groups for: (a) support during casual employment; (b) accessing collegial support from pre-existing connections, in particular those formed during initial teacher education; and (c) support to deal with challenging situations such as a hostile work environment. The paper suggests that further research needs to be conducted into how private media groups might support the needs of early career teachers.
Kinesthetic Teaching via Trajectory Optimization with Common Distance Metrics

We propose a method for learning skills by learning kinesthetic skills via trajectory optimization. We recreate demonstrations by optimizing the trajectory to be near the demonstration. We use the trajectory optimizer, TrajOpt to turn distance metrics, the Frechet and Hausdorff distances, into cost functions that penalize a trajectory from deviating from the demonstration. Using a few techniques to assist the optimization process, we are able to reliable recreate provided demonstrations.
Pricing and Referrals in Diffusion on Networks

When a new product or technology is introduced, potential consumers can learn its quality by trying it, at a risk, or by letting others try it and free-riding on the information that they generate. We propose a dynamic game to study the adoption of technologies of uncertain value, when agents are connected by a network and a monopolist seller chooses a profit-maximizing policy. Consumers with low degree (few friends) have incentives to adopt early, while consumers with high degree have incentives to free ride. The seller can induce high-degree consumers to adopt early by offering referral incentives – rewards to early adopters whose friends buy in the second period. Referral incentives thus lead to a ‘double-threshold strategy’ by which low and high-degree agents adopt the product early while middle-degree agents wait. We show that referral incentives are optimal on certain networks while inter-temporal price discrimination is optimal on others.
Automatic keyphrase extraction and ontology mining for content‐based tag recommendation

Collaborative tagging represents for the Web a potential way for organizing and sharing information and for heightening the capabilities of existing search engines. However, because of the lack of automatic methodologies for generating the tags and supporting the tagging activity, many resources on the Web are deficient in tag information, and recommending opportune tags is both a current open issue and an exciting challenge. This paper approaches the problem by applying a combined set of techniques and tools (that uses tags, domain ontologies, keyphrase extraction methods) thereby generating tags automatically. The proposed approach is implemented in the PIRATES (Personalized Intelligent tag Recommender and Annotator TEStbed) framework, a prototype system for personalized content retrieval, annotation, and classification. A case study application is developed using a domain ontology for software engineering. © 2010 Wiley Periodicals, Inc.
Voice conversion for unknown speakers

Voice conversion is a technique for modifying a source speaker’s speech to sound as if it was spoken by a target speaker. The conventional solutions to this problem are based on training and applying conversion functions which require a substantial amount of training data from both the source and the target speaker. In this paper, we present a voice conversion technique that requires no preexisting training data from the source speaker. This new approach uses a speech recognizer to index the target training data so that each unknown source frame can be used to retrieve similar frames from the target database. The retrieved frames are then used to estimate conversion functions in a similar way to conventional methods. The paper presents both objective and subjective evaluations of the method. It also explores a number of variants including the contrast between using single and multiple transforms, and between the cases where the content of the source speech is known or unknown. The overall conclusion of the paper is that the method presented can result in identification of the target speaker with as little as a single sentence of source data to transform, however, knowledge of the source orthography is needed to attain a close similarity.
Advances of Research in Fuzzy Integral for Classifiers' fusion

Fuzzy Integral is compared with other two methods which are hot in studying of classifiers' fusion. The standard model of fuzzy integral and its general solution are introduced. Then, the state of the art and the challenge problems in fuzzy integral research field are discussed. The algorithms for standard and extended fuzzy integral models are briefly analyzed. Finally, the open areas of theoretic and applied research in fuzzy integral are brought forward.
A case study on timed supervisory control on a linear cluster tool using aggregated timed synthesis

In this paper an approach to distributed timed supervisory based on aggregated synthesis is given. This paper formulates the distributed timed control problem and introduces a method for finding the distributed timed supervisor with the minimal makespan with reference to other supervisors synthesized with aggregated timed synthesis. The method is based on bottleneck optimization used in manufacturing lines. After introduction of the aggregated timed synthesis, the synthesis is applied to a linear cluster tool in order to compare the introduced bottleneck optimization with nonoptimized aggregated time synthesis.
Vision-Based Autonomous Vehicle Guidance in Indoor Environments Using Odometer and House Corner Location Information

An approach to vehicle guidance using odometer and house corner location information is proposed for visionbased vehicle navigation in indoor environments. A small vehicle with wireless control and image grabbing capabilities is used as a test bed. A camera with the pantilt- zoom capability is equipped on the vehicle. The vehicle is guided to follow a path consisting of nodes learned in advance, which include the information of travel distances and turning angles provided by the odometer. A vehicle location method using house corners is proposed to estimate vehicle location which can be used to correct vehicle location, thus preventing mechanic error accumulation in long-distance navigation. Good experimental results show the feasibility of the proposed approach.
Functional cache simulator for multicore

This paper presents the design and construction of a light-weight execution-driven cache simulator for multicore CPU. We implement the simulator as a Pin tool, running on top of Pin, a dynamic instrumentation tool developed by Intel. We then use it to study the cache behavior of 13 multithreaded programs selected from the two well-recognized benchmark suites, SPLASH2 and PARSEC. We plan to release the tool as open-source software and hope that the computer architecture research community would benefit from it.
Working Set-based Physical Memory Ballooning

Minimizing the total amount of physical memory consumption of a set of virtual machines (VM) running on a physical machine is the key to improving a hypervisor’s consolidation ratio, which is defined as the maximum number of VMs that can run on a server without any performance degradation. To give each VM just enough physical memory equal to its true working set (TWS), we propose a TWS-based memory ballooning mechanism that takes away all unneeded physical memory from a VM without affecting its performance. Compared with a state-of-the-art commercial hypervisor, this working setbased memory virtualization technique is able to produce noticeably more effective reduction in physical memory consumption under the same input workloads, and thus represent promising additions to the repertoire of hypervisor-level optimization technologies.
Towards compensation for radial lens distortion using radial basis functions

Many of the algorithms in computer vision are based on the assumption of linear imaging process, i.e. the world point, image point and the camera center are collinear. However, this assumption is sometimes violated in the case of real lenses. This phenomenon, known as radial lens distortion, can easily be characterized by straight scene lines imaged as curved lines. The most frequently used remedy for this distortion is to model it using some simple equations. The parameters of these equations are then computed by various methods and reported as a part of calibration parameters of the camera under consideration. This paper, however, proposes a novel approach which does not impose any specific model on radial lens distortion. Instead, it tries to learn the distortion pattern from a sample image and then compensate for it. To this end, the proposed method exploits a special class of functions known as Radial Basis Functions to learn the distortion pattern of straight scene lines. Also, some examples have been provided to illustrate the efficiency of the proposed method.
Collecting positive instances of “instance-of” relationship in the Persian language

Fetching Lexico-Syntactic patterns from text rely on pairs of words (positive instances) that represent the target relation, and finding their simultaneous occurrence in text corpus. Due to existence of WordNet thesaurus (which contains the semantic relationship between words), collecting positive instances is easy. In non-english languages, it's hard to collect large number of positive instances in various contexts. We investigated some new ideas for collecting them in Persian language and finally run the best one and collected approximately 6,000 positive instances.
Design Patterns for Cyber-Physical Systems: The Case of a Robotic Greenhouse

Cyber-physical systems (CPS) are a new discipline of research that involves electrical engineering, electronics, computer science, control and communications interacting with physical processes. This leads to a co-managed domain where both worlds (cyber and physical) must be taken into account to decide the actions to take. In this sense, this paper presents the design of a robotic greenhouse, which involves basically the greenhouse and a mobile robot interacting with the physical environment (collecting data and acting on plants). Thus, the main contribution is related to taking practical experience and generalize it by applying well-proven techniques to the development of CPS.
Using Genetic Algorithms to Solve the Tactical Fixed Interval Scheduling Problem

Discrete optimization problems are usually NP hard. The structural characteristics of these problems significantly dictate the solution landscape. In this paper, we explore a structured approach to solving these kinds of problems. We use a reinforcement learning system to adaptively learn the structural characteristics of the problem, hereby decomposing the problem into several subproblems. Based on these structural characteristics, we develop a Genetic Algorithm by using structural operations to recombine these subproblems together to solve the problem. The reinforcement learning system directs the GA. We test our algorithm on the Tactical Fixed Interval Scheduling Problem(TFISP) which is the problem of determining the minimum number of parallel non-identical machine such that a feasible schedule exists for a given set of jobs. This work continues our work in exploiting structure for optimization(Zhong & Santos 1999).
Petrifying Operating Guidelines for Services

Operating guidelines characterize correct interaction (e.g. deadlock freedom) with a service. They can be stored in a service registry. So far, they have been represented as an annotated transition system. For the sake of saving space in the registry, we want to translate operating guidelines into Petri nets. To make this possible, we carefully investigate regularities in the annotations.
Joint Transition-based Dependency Parsing and Disfluency Detection for Automatic Speech Recognition Texts

Joint dependency parsing with disﬂuency detection is an important task in speech language processing. Recent methods show high performance for this task, although most authors make the unrealistic assumption that input texts are transcribed by human annotators. In real-world applications, the input text is typically the output of an automatic speech recognition (ASR) system, which implies that the text contains not only disﬂuency noises but also recognition errors from the ASR system. In this work, we propose a parsing method that handles both disﬂuency and ASR errors us-ing an incremental shift-reduce algorithm with several novel features suited to ASR output texts. Because the gold dependency information is usually annotated only on transcribed texts, we also introduce an alignment-based method for transferring the gold dependency annotation to the ASR output texts to construct training data for our parser. We conducted an experiment on the Switchboard corpus and show that our method outperforms conventional methods in terms of dependency parsing and disﬂuency detection.
Recursive blind symbol estimation of convolutionally coded cochannel signals

A recursive blind equalizer is presented that directly estimates the transmitted symbols of multiple cochannel signals in the presence of ISI. The algorithm exploits shift structure present in the data model and the finite alphabet property of the signals. The proposed method possesses a separation property that allows the symbol sequences for each user to be estimated independently of the others. Problematic issues surrounding unknown and mismatched channel lengths for the cochannel users can be handled effectively in the recursive equalizer. Additionally, if the cochannel signals are encoded prior to transmission, we show how the code structure can be incorporated into the recursive equalizer to improve its performance.