Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of electronic public procurement solution for the Republic of Macedonia

As EU directives require that all administration should perform their procurement processes electronically by 2010, an open-source application framework for building electronic public procurement (ePP) solutions was developed within the ELLECTRA-WeB project. As a part of this project the Republic of Macedonia adapted the ePP solution to support the public procurement lifecycle. This paper presents an assessment of the customized ePP solution for the Republic of Macedonia with a well-defined evaluation framework that follows the user through the phases of electronic procurement procedures. The objective of the study is to present the results of applying different evaluation methods to determine the quality, functionality and efficiency of the ePP solution that meet EU requirements.
Challenges in preparing aspiring principals for instructional leadership: voices from the field

ABSTRACT The purpose of this qualitative study was to examine the challenges professors of educational leadership encounter as they prepare aspiring principals to be instructional leaders. Drawing upon an open-ended questionnaire and semi-structured interviews of professors from across the U.S.A. , we analyzed significant statements and created themes that illuminate the challenges facing university faculty in preparing aspiring principals for instructional leadership. From the analysis of the combined data sources, five overarching challenges emerged for preparing future principals for the role of instructional leader. These challenges are: (a) teaching instructional leadership via online courses, (b) time in the internship to focus on instructional leadership, (c) instructional leadership mindsets, (d) pedagogical knowledge for use in instructional leadership and (e) the professor’s knowledge/experience in instructional leadership. The five challenges presented in the findings highlight the need for continuous program evaluation and collaboration among faculty to address coursework both in the classroom and in the internship. Additionally, the challenges highlight the need for more research in how to prepare aspiring principals to be culturally responsive and adopt a growth-mindset when it comes to teacher development, and how to create powerful learning experiences for aspiring principals in varying program contexts.
Energy conservation in asynchronous systems using self-adaptive fine-grain voltage scaling

Dynamic voltage scaling is a technique for lowering energy consumption in microelectronic systems when the performance required is at times lower than the system's peak capability. This paper proposes a new voltage scaling technique for asynchronous systems which allows fine-grain scaling of supply voltage, i.e. each stage in the pipeline is allowed to scale its voltage independently. Instead of applying a single scaled supply voltage to all the stages in a pipeline, each stage operates at a voltage which is just sufficient for it to run at a throughput supported by the pipeline. This results in higher energy savings as stages with smaller latency can now run at a lower voltage than the voltage required by higher latency stages. Voltage scaling for each stage is guided by local `congestion-starvation' information generated only from its handshake signals, not requiring any global coordination. The technique described in this paper is self-adaptive, on-chip, and allows continuous scaling of voltage levels. It allows choosing appropriate level of voltage scaling granularity with desired energy-area trade-offs for a given system. Fine-grain approach has the advantage of more energy savings while it has higher area overhead as compared to a coarse-grain approach. The proposed technique is applicable to linear pipelined systems, as well as systems with more complex topologies, including pipeline constructs for parallel, conditional, and repetitive computation. We present hardware designs of voltage controller and voltage regulator used in our technique. We simulated our technique at behavioral level in Verilog for five algorithms modeled as micropipelines and simulation results show that our technique achieves upto 85% dynamic energy savings with a throughput degradation of less than 10%.
Poor confinement of e-h recombination zone in blue oleds

The possibility of making large light-emitting area with low cost has stimulated the research work on organic-based light-emitting devices (OLEDs). OLEDs with emission color covering almost the entire visible range have been realized. This was achieved by simultaneously synthesizing new materials and optimizing the device structure itself by adding/getting rid of different layers. Recently, OLEDs performance has improved and has become adequate for many display applications. However, the main problem still to be solved, concerns the short lifetime of the devices. Surprisingly, there are only few fundamental works on the degradation mechanisms of OLEDs. The mechanisms behind the light decay and the color shifts, especially for blue colour, are still not fully understood. Earlier studies have suggested that the poor balance between the number of electrons and holes in the emitting layer, is one of the main factors in limiting the lifetime of the device.
Privacy-constrained remote source coding

We consider the problem of revealing/sharing data in an efficient and secure way via a compact representation. The representation should ensure reliable reconstruction of the desired features/attributes while still preserve privacy of the secret parts of the data. The problem is formulated as a remote lossy source coding with a privacy constraint where the remote source consists of public and secret parts. Inner and outer bounds for the optimal tradeoff region of compression rate, distortion, and privacy leakage rate are given and shown to coincide for some special cases. When specializing the distortion measure to a logarithmic loss function, the resulting rate-distortion-leakage tradeoff for the case of identical side information forms an optimization problem which corresponds to the “secure” version of the so-called information bottleneck.
Segmentation by combining parametric optical flow with a color model

We present a simple but efficient model for object segmentation in video scenes that integrates motion and color information in a joint probabilistic framework. Optical flow is modeled using parametric motion with Gaussian noise. The color distribution of foreground and background is described by histograms or Gaussian mixture models. Optimization is carried out using an efficient graph cut algorithm. In quantitative experiments on a variety of video data, we demonstrate that the proposed approach leads to significant reductions in error rates compared to a state-of-the-art motion-only segmentation.
Activation of Myenteric Glia during Acute Inflammation In Vitro and In Vivo

Background Enteric glial cells (EGCs) are the main constituent of the enteric nervous system and share similarities with astrocytes from the central nervous system including their reactivity to an inflammatory microenvironment. Previous studies on EGC pathophysiology have specifically focused on mucosal glia activation and its contribution to mucosal inflammatory processes observed in the gut of inflammatory bowel disease (IBD) patients. In contrast knowledge is scarce on intestinal inflammation not locally restricted to the mucosa but systemically affecting the intestine and its effect on the overall EGC network. Methods and Results In this study, we analyzed the biological effects of a systemic LPS-induced hyperinflammatory insult on overall EGCs in a rat model in vivo, mimicking the clinical situation of systemic inflammation response syndrome (SIRS). Tissues from small and large intestine were removed 4 hours after systemic LPS-injection and analyzed on transcript and protein level. Laser capture microdissection was performed to study plexus-specific gene expression alterations. Upon systemic LPS-injection in vivo we observed a rapid and dramatic activation of Glial Fibrillary Acidic Protein (GFAP)-expressing glia on mRNA level, locally restricted to the myenteric plexus. To study the specific role of the GFAP subpopulation, we established flow cytometry-purified primary glial cell cultures from GFAP promotor-driven EGFP reporter mice. After LPS stimulation, we analyzed cytokine secretion and global gene expression profiles, which were finally implemented in a bioinformatic comparative transcriptome analysis. Enriched GFAP+ glial cells cultured as gliospheres secreted increased levels of prominent inflammatory cytokines upon LPS stimulation. Additionally, a shift in myenteric glial gene expression profile was induced that predominantly affected genes associated with immune response. Conclusion and Significance Our findings identify the myenteric GFAP-expressing glial subpopulation as particularly susceptible and responsive to acute systemic inflammation of the gut wall and complement knowledge on glial involvement in mucosal inflammation of the intestine.
Gating improves neural network performance

Our first purpose is to study the performance of gating network functions in a committee machine setting. The problem of image deblurring is used to test the capability of such a system. Input clustering divides the task of deblurring into several subtasks. Each subtask is performed by a projection pursuit learning network (Basu and Su, 1999). We use a dynamic gating structure to combine outputs from various committee members. Our second purpose is to study the possibility of extending the role of the input signal beyond the decision making stage in the gating structure. Input data contain crucial structural information and characteristics of the data in a degraded form. The novel aspect of this work is the use of input signal with the output from the gating structure to produce the overall output. Resulting images show significant improvement over images that are produced from the output of the gating structure alone.
Voltage sag detection technique for a dynamic voltage restorer

Dynamic voltage restorers (DVRs) are used to protect sensitive loads from the effects of voltage sags on the distribution feeder. This paper presents and verifies a novel voltage sag detection technique for the control system of a DVR. In some cases it may be necessary for the DVR control system to not only detect the start and end of a voltage sag but also to determine the sag depth and any associated phase shift. The DVR, which is placed in series with a sensitive load, must be able to respond quickly to a voltage sag if end users of sensitive equipment are to experience no voltage fluctuations. A problem arises when fast evaluation of these parameters is required, as standard information tracking or detection methods such as the Fourier transform or phase-locked-loop (PLL) are too slow. As a result of this the voltage sag detection method this paper proposes a new state-space matrix method, which is able to compute the phase shift and voltage reduction of the supply voltage much quicker than the Fourier transform or a PLL. The paper also illustrates that the state space matrix method returns results that can be directly interpreted whereas other methods such as the wavelet transform, which return results that can be difficult to interpret.
Evolving Turing Complete representations

Standard GP, chiefly concerned with evolving functions, which are mappings from inputs to output, is not Turing Complete. We raise issues resulting from attempts at extending standard GP to Turing Complete representations. Firstly, there is a problem when a contiguous piece of code is moved to a new location (in a different program) by crossover. In general its functionality will be altered if global memory is used, as other parts of the program may access the same piece of memory. Secondly, traditional crossover does not respect modules. Crossover can disrupt a group of instructions that were working together (e.g. in the body of a loop) in one parent, but end up separated in two different offspring after reproduction. A crossover operator is proposed that only operates at the boundaries of modules. The identification of module boundaries is made easy by using a representation in which explicit modules are denned, in contrast with other representations where the module boundaries would have to be identified by some other means. The halting problem is a central issue, however as a consequence of this crossover operator we are more likely to produce self terminating programs, thus saving time when testing.
A proximal alternating direction method for $\ell_{2,1}$-norm least squares problem in multi-taskfeature learning

The joint feature selection problem arises in many fields including computer vision, text classification and biomedical informatics. Generally, recent results show that it can be realized by solving a $\ell_{2,1}$-norm involved minimization problem. However, solving the optimization problem is a challenging task due to the non-smoothness of the regularization term. In this paper, we reformulate the problem to an equivalent constrained minimization problem by introducing an auxiliary variable. We split the corresponding augmented Lagrange function and minimize the subproblem alternatively with one variable by fixing the other one. Moreover, we linearize the subproblem and add a proximal-point term when the closed-form solutions are not easily to derived. The convergence analysis and the relatedness with other algorithms are also given. Although the $\ell_{2,1}$-norm is mainly considered, we show that the $\ell_{\infty,1}$-norm penalized learning problem can also be readily solved in our framework. The reported experiments on simulated and real data sets show that the proposed method is effective and promising. The performance comparisons illustrate that the proposed algorithm is competitive with even performs little better than the state-of-the-art solver SLEP.
Subjectivity Recognition on Word Senses via Semi-supervised Mincuts

We supplement WordNet entries with information on the subjectivity of its word senses. Supervised classifiers that operate on word sense definitions in the same way that text classifiers operate on web or newspaper texts need large amounts of training data. The resulting data sparseness problem is aggravated by the fact that dictionary definitions are very short. We propose a semi-supervised minimum cut framework that makes use of both WordNet definitions and its relation structure. The experimental results show that it outperforms supervised minimum cut as well as standard supervised, non-graph classification, reducing the error rate by 40%. In addition, the semi-supervised approach achieves the same results as the supervised framework with less than 20% of the training data.
Automatic Domain-Specific Sentiment Lexicon Generation with Label Propagation

Nowadays, the advance of social media has led to the explosive growth of opinion data. Therefore, sentiment analysis has attracted a lot of attentions. Currently, sentiment analysis applications are divided into two main approaches, the lexicon-based approach and the machine-learning approach. However, both of them face the challenge of obtaining a large amount of human-labeled training data and corpus. For the lexicon-based approach, it requires a sentiment lexicon to determine the opinion polarity. There are many existing benchmark sentiment lexicons, but they cannot cover all the domain-specific words meanings. Thus, automatic generation of a domain-specific sentiment lexicon becomes an important task. We propose a framework to automatically generate sentiment lexicon. First, we determine the semantic similarity between two words in the entire unlabeled corpus. We treat the words as nodes and similarities as weighted edges to construct word graphs. A graph-based semi-supervised label propagation method finally assigns the polarity to unlabeled words through the proposed propagation process. Experiments conducted on the microblog data, Twitter, show that our approach leads to a better performance than baseline approaches and general-purpose sentiment dictionaries.
Housing Decisions in a Simple World : a baseline model 1

Given uncertainty, housing decisions are complicate d by imperfect /asymmetric information and irreversibility. Instability will b e exacerbated by liquidity constraints and institutional change and magnified by herding and c onstraints on house-buyers’ rationality, including cognitive bias and other soc io-psychological factors. In this paper, the behavioural aspects of housing decisions are as s ssed focusing on how cognitive constraints and herding behaviour can be linked to housing market instability. The hypotheses about herding are tested econometrically using neighbourhood statistics for England. The evidence reveals that housing demand c be effectively modelled as the outcome of herding tendencies as well as the tradit ionally emphasized objective factors. In conventional analyses of housing demand, the use r cost of housing is identified as a key variable determining individual s’ decisions to own housing assets. User cost is a simple measure of the relative cost of housing assets, expressed in terms of real interest rates, imputed rents and capital a ppreciation (or depreciation). These analyses assume a risk-free world of complete infor mation, and homogenous / atomistic agents. However, in a world of uncertain ty fi ancing constraints, speculative forces and institutional changes will m odify the influence of the user cost of housing on decision making, generating destabili zing and volatile patterns in housing demand. Ortalo-Hagne and Rady (2002) argue that institution al changes encouraging owner occupation will affect housing demand by ampl ifying market volatility. There is some anecdotal evidence consistent with this ass ertion: following the Thatcherite reforms implemented in the UK during the 1990s ther e were significant changes in the patterns of home ownership: owner-occupation rates increased from 57% in 1981 to 70% in 2001 (Mew et al 2003, p.12). This period of institutional change w as accompanied by extreme volatility in the housing ma rket. Other institutional changes have had influences, for example the period of fina ncial deregulation in the UK introduced volatility in financing constraints. Hou sing decisions will be affected by limits on borrowing even if the user cost of housin g is relatively low and in the period following financial deregulation in the early 1980s , financing constraints were relaxed fuelling an unsustainable housing boom, which colla psed again with the tightening of monetary conditions in the early 1990s. This paper aims theoretically and empirically to as ses these housing market complexities and link them into behavioural complex ity. In Section 1, a baseline model of a ‘perfect’ housing market is presented. S ome of the complicating characteristics of housing are introduced in Sectio n 2, leading on to an analysis in Section 3 of the links between these complexities, h rding behaviour and housing market instability. In Section 4, these destabilizi ng forces are analysed econometrically using 2002 ward level housing marke t data for England and Wales. Conclusions and policy implications are presented i n Section 5.
Social marginalization and chronic illness: A critical analysis of the role of labour-market exclusion

Since the 1990s, most European countries have implemented activation policies targeting the unemployed. During the past decade, the target group for activation policies expanded and currently also includes persons with limitations in their ability to work due to long-term or chronic illness and disabilities. The argument underlying these policies is that labour-market exclusion is the main cause for social marginalization because participation in paid work provides important social and psychological functions that cannot be found elsewhere. Based on an extensive set of quantitative data that combines register data and survey data, and using structural equation modelling, this paper analyses the relationship between chronic illness and social marginalization, and in particular which role labour-market exclusion plays in this relationship. Is labour-market exclusion a crucial factor in explaining why individuals with chronic illnesses face a higher risk of social marginalization if factors such as income and education are also taken into account? From the statistical results, the paper states that individuals with chronic illnesses face a far higher risk of social marginalization, but that this risk is caused by their health limitations and not by their lack of labour-market participation. Contrary to the policies’ logic and the theoretical argument of psycho-social theories originating from the deprivation perspective, no direct, indirect or mediating effects of labour-market exclusion on social marginalization were identified.
Banked Register Files for SMT Processors

Multiported register files are a critical component of high-performance superscalar microprocessors. Deeper pipeline speculation and higher instruction-level parallelism (ILP) of current processor designs push a growing requirement on both the number of ports and the number of registers. These increasing demands on register files cause the area of a conventional multiported regfile to grow more than quadratically with issue width [7]. The trend towards simultaneous multithreading (SMT) further increases register count as separate architectural registers are needed for each thread. For example, the proposed eight-issue Alpha 21464 design had a regfile that occupied over five times the area of the 64 KB primary data cache [3]. Hence, we examine the designs of banked multiported register files that consist of multiple interleaved banks of fewer ported register cells to reduce power, area, and access time. In this talk, we will present results that extend our previous work [4] on banked register files to SMT processors. Banked register files designs have been shown to provide sufficient bandwidth for a superscalar machine, but previous proposed designs had complex control structures that would likely limit cycle time and add to design complexity [6, 1, 2]. We present a banked multiported regfile design together with a much simpler and faster control logic suitable for a deeply pipelined high-frequency superscalar processor [4]. Our control scheme does not place any register bank arbitration in the critical wakeup-select loop but instead speculatively issues potentially conflicting instructions. Bank conflicts occurs when too many instructions are trying to read or write the same bank at the same cycle. If any conflicts are found after issue, a pipelined recovery scheme quickly repairs the issue window and reissues conflicting instructions. In contrast to previous work [6, 1, 2], all conflicts are detected and resolved in one pipeline stage such that no write buffering or pipeline stalls are required. The extra pipeline stage used for port arbitration and the possibility of bank conflicts in our design can impact processor performance. The additional pipeline stage causes an increase in branch misprediction latency while instances of a bank conflict add penalty cycles to repair the pipeline and delays the issuing of dependent instructions. Fortunately, the number of bank conflicts can be kept within a few percent of total instructions if we can remove the correlation between accesses to the same bank. It is observed that instructions which become ready in the same cycle tend to be issued together and some architectural registers are used more frequently than others. To avoid unnecessary read port contention and to remove the correlation between the same-cycle