Miracle-dz/UniFAR-FTUs huggingface dataset:
Global supply chains and macroeconomic relationships in Asia

This timely book deploys new tools and measures to understand how global production networks change the nature of global economic interdependence, and how that in turn changes our understanding of which policies are appropriate in this new environment. Bringing to bear an array of the latest methods and data to study global value chains, this unique book assesses the evolution of global value chains at the firm level, and how this affects competitiveness in Asia.
Gravitational transform for data clustering - application to multicomponent image classification

In this communication, we introduce the concept of gravitational transform, with application to multicomponent image classification. This general concept, from which many settings can be implemented, takes into account both feature sets extracted from the image and the spatial distance between pixels in order to further improve the classification, and thus the partitioning into homogeneous regions. Examples of classification and comparative results with synthetic data are presented, showing that this approach improves the classification rates in all cases, independently of the chosen classification technique, and outperforms some recent approaches in contextual multidimensional unsupervised clustering. The encouraging results obtained make this technique a valuable tool to insert between the feature extraction process and the unsupervised classification process.
A Combined Antenna Arrays and Reverse-Link Synchronous DS-CDMA System over Frequency-Selective Fading Channels with Power Control Error

An improved antenna array (AA) has been introduced, in which reverse-link synchronous transmission technique (RLSTT) is incorporated to effectively make better an estimation of covariance matrices at a beamformer-RAKE receiver. While RLSTT is effective in the first finger at the RAKE receiver in order to reject multiple-access interference (MAI), the beamformer estimates the desired user's complex weights, enhancing its signal and reducing cochannel interference (CCI) from the other directions. In this work, it is attempted to provide a comprehensive analysis of user capacity which reflects several important factors such as the shape of multipath intensity profile (MIP), the number of antennas, and power control error (). Theoretical analysis, confirmed by the simulations, demonstrates that the orthogonality provided by employing RLSTT along with AA may make the DS-CDMA system insensitive to the even with fewer numbers of antennas.
Evolving artificial neural network using simple augmenting weight matrix method

In this paper, a simple method is proposed to evolve artificial neural networks(ANNs) using augmenting weight matrix method(AWMM). ANNs' architecture and connection weights can be evolved simultaneously by AWMM, and their structures incrementally are growing up from minimal structure. It is a non-mating method. It employs 5 mutation operators: add connection, add node, delete connection, delete node, and new initial weight. And the connection weight is trained by the simplified alopex method, which is a correlation based method for solving optimization problem. In AWMM, structural information is encoded to weighting matrix, and the matrix is augmenting as the hidden nodes are added.
Automated motif discovering in RNA molecules

We used a novel graph-based approach to identify recurrent RNA tertiary motifs embeddedwithin secondary structure. We catalogued all the secondary structural elements of the RNA molecule and clustered them using an innovative graph similarity measure. We applied our method to three widely studied structures: H.m 50S, E.coli 50S and T.th 16S. We identified 10 known motifs without any prior knowledge of their shapes or positions. We additionally identified four putative new motifs.
Towards a Typology of Virtual Communities of Practice

The literature is packed with “one-size-fits-all” advice on how to develop intentionally formed virtual communities of practice (VCoPs). However, a closer look at the literature shows that VCoPs often have unique “personalities. ” Based on an extensive review of the literature and a study of 18 VCoPs, we built a typology containing 21 structuring characteristics. We then used this typology and three of the studied VCoPs to show how different their basic natures are. Researchers and practitioners alike must not only recognize the diversity of VCoPs, but also identify challenges, strategies and practices that are contingent upon their specific characteristics.
DAISY : A database for identification of systems

We point out the existence of a disturbing deficiency in the field of system identification, namely the fact that many results, published in papers, are not reproducible. In many cases, datasets and time series, that are used to illustrate identification methods and algorithms in these publications, are not freely available. We propose to remedy this serious deficiency by setting up a publically accessible website, called DAISY, to which authors can submit datasets that are used to illustrate certain claims and algorithms in their papers. Several additional benefits are discussed as well.
Workflow Deployment on Collaborative Cloud Platforms

Recently, the widespread adoption of ICT within the cities is significantly changing the way people work and interact with the public administrations. The idea of smart city is becoming very popular. It indicates all those cities where innovative and efficient public services and/or applications are integrated within both mid- and long-term plans of sustainable urban development and economic growth. The EU, is devoting constant efforts to devise a strategy for achieving urban growth in a smart sense for its metropolitan city-regions. PRISMA in a good example of such effort: it designs and implements an interoperable and collaborative cloud platforms for the Public Administrations (PAs). The concept of workflow is the central focus on PRISMA: each administrative process is viewed as a workflow that has to deployed on one or more PA data centers. The performance of the whole infrastructures strongly depend on the strategy for the workflow orchestration and deployment. In this paper the authors have introduced a new approach for managing the deployment of workflows on PRISMA. It is implemented in IWoM, the novel workflow management system for IaaS environments realized for the PRISMA project. The simulations campaign have shown that the use of this optimization strategy can strongly improve the time spent for the deployment process.
Impaired facial emotion recognition in early-onset right mesial temporal lobe epilepsy

Background: Anteromedial temporal lobe regions, particularly the amygdala, participate in the recognition of emotions from facial expressions. The authors studied the ability of facial emotion recognition (ER) in subjects with symptomatic epilepsy, evaluating whether mesial temporal lobe damage is related to an impairment in the recognition of specific emotions and whether the onset of seizures in a critical period of life could prevent the development of ER. Methods: Groups included patients with temporal lobe epilepsy (TLE) with MRI evidence of mesial temporal sclerosis (MTS) (n = 33); patients with TLE with MRI evidence of temporal lobe lesions other than MTS (n = 30); and patients with extratemporal epilepsy (n = 33). Healthy volunteers (n = 50) served as controls. ER was tested by matching a facial expression with the name of one of the following basic emotions: happiness, sadness, fear, disgust, and anger. A face-matching task was used to control visuoperceptual abilities with face stimuli. Results: No subject showed deficits in the face-matching task. ER was impaired in patients with right MTS, especially for fearful faces. Patients presenting left MTS, right or left temporal lobe lesions other than MTS, or extratemporal seizure foci showed ER performances similar to controls. In all subjects with right TLE, the degree of emotion recognition impairment was related to age at first seizure (febrile or afebrile) and age at epilepsy onset. Conclusions: Early-onset right-sided mesial temporal lobe epilepsy is the key substrate determining a severe deficit in recognizing emotional facial expressions, especially fear.
Using Causal Mapping Methods to Identify and Analyse Risk in Information System Projects as a Post-Evaluation Process

Software systems developments have become more difficult and complex with new technology being introduced rapidly and the increasing complexity of the marketplace. This paper proposes a post-evaluation framework to identify causes of shortfalls in previously implemented information system projects. This framework has been used on a case study to produce causal diagrams using a template. The template reflects the different generic stages of the system development life cycle. The method builds on the reflection and review of failure factors in cause and effect diagrams, and is useful for facilitating learning within an organisation as a method of project post-evaluation. An approach is described for identifying information system risk areas in future projects. Key word: Causal Mapping, Post-evaluation, Lessons Learned, Information System Risk and System Development Life Cycle.
‘More of a Cause’: Recent Work on Degrees of Causation and Responsibility

It’s often natural to compare two events by describing one as ‘more of a cause’ of some effect than the other. But what do such comparisons amount to, exactly? This paper aims to provide a guided tour of the recent literature on ‘degrees of causation’. Section 2 looks at what I call ‘dependence measures’, which arise from thinking of causes as difference-makers . Section 3 looks at what I call ‘production measures’, which arise from thinking of causes as jointly sufficient for their effects. Finally, section 4 examines the important question of whether there is any sense in which an agent is more responsible for an outcome in virtue of her action being more of a cause of it. I describe a puzzle that emerges from this question, first discussed by Bernstein (2017) and Sartorio (2015), and explore various strategies for resolving it. are an manufacturing who all vote replace the outdated equipment. equipment later malfunctions, injuring an employee.
2 RED with ACK Spoofing 2 . 1 Principles of ACK Spoofing

We propose a new congestion signaling method to support Active Queue Management in IP networks called ACK (acknowledgement) Spoofing. ACK Spoofing enables the router to reduce the transmission rate for any responsive TCP source without needlessly reducing goodput (by dropping data packets in advance of buffer overflow), nor relying on TCP implementations to support the packet marking required by Explicit Congestion Notification (ECN). Instead, ACK Spoofing only uses the available fast retransmit and fast recovery mechanisms in current TCP implementations. The basic idea of ACK Spoofing is that a router can remotely trigger the fast retransmit and fast recovery mechanisms in current TCP implementations to reduce their respective transmission rates by sending multiple locally-created duplicate ACK packets to the TCP source. The only real difficulty is determining the correct state information (i.e., ack number and advertised window) to include in the spoofed ACKs, for which we present our solution. ACK Spoofing can be combined with any AQM algorithms, including Random Early Detection (RED). Using simulation, we compare RED/Spoofing with Tail Drop, RED, and RED/ECN. Our results show that RED/Spoofing performs as well as RED/ECN, even though it avoids ECN’s deployment problem. In addition, RED/Spoofing out-performs RED, in terms of packet loss rate and fairness, and significantly reduces the queueing delays in comparison to Tail Drop.
On the Performance of Primal/Dual Schemes for Congestion Control in Networks with Dynamic Flows

Stability and fairness are two design objectives of congestion control mechanisms; they have traditionally been analyzed for long-lived flows (or elephants). It is only recently that short-lived flows (or mice) have received attention. Whereas stability has been established for the existing primal-dual based control mechanisms, the performance issue has been largely overlooked. In this paper, we study utility maximization problems for networks with dynamic flows. In particular, we consider the case where sessions of each class results in flows that arrive according to a Poisson process and have a length given by a general distribution. The goal is to maximize the long-term expected system utility that is a function of the number of flows and the rate (identical within a given class) allocated to each flow. Our results show that, as long as the average amount of work brought by the flows is strictly within the network stability region, the rate allocation and stability issues are decoupled. While stability can be guaranteed by, for example, a FIFO policy, utility maximization becomes an unconstrained optimization that results in a static rate allocation for flows. We also provide a queueing interpretation of this seemingly surprising result and show that not all utility functions make sense for dynamic flows. Finally, we use simulation results to show that indeed the open-loop algorithm maximizes the expected system utility.
Accounting for unequal variances in evolutionary trend mechanisms

Evolutionary trends seemingly abound throughout the history of life. Perhaps the best-known example is Cope's rule, the tendency for lineages to increase in size over time. Other examples include hypothesized trends toward increasing complexity and fitness. What kinds of mechanisms cause such large-scale trends—trends occurring in higher taxa over geological time spans—and how can we effectively visualize them?
RACK-1 regulates let-7 microRNA expression and terminal cell differentiation in Caenorhabditis elegans

The let-7 microRNA (miRNA) regulates cell cycle exit and terminal differentiation in the C. elegans heterochronic gene pathway. Low expression of let-7 results in retarded vulva and hypodermal cell development in C. elegans and has been associated with several human cancers. Previously, the versatile scaffold protein receptor for activated C kinase 1 (RACK1) was proposed to facilitate recruitment of the miRNA-induced silencing complex (miRISC) to the polysome and to be required for miRNA function in C. elegans and humans. Here, we show that depletion of C. elegans RACK-1 by RNAi increases let-7 miRNA levels and suppresses the retarded terminal differentiation of lateral hypodermal seam cells in mutants carrying the hypomorphic let-7(n2853) allele or lacking the let-7 family miRNA genes mir-48 and mir-241. Depletion of RACK-1 also increases the levels of precursor let-7 miRNA. When Dicer is knocked down and pre-miRNA processing is inhibited, depletion of RACK-1 still leads to increased levels of pre-let-7, suggesting that RACK-1 affects a biogenesis mechanism upstream of Dicer. No changes in the activity of the let-7 promoter or the levels of primary let-7 miRNA are associated with depletion of RACK-1, suggesting that RACK-1 affects let-7 miRNA biogenesis at the post-transcriptional level. Interestingly, rack-1 knockdown also increases the levels of a few other precursor miRNAs. Our results reveal that RACK-1 controls the biogenesis of a subset of miRNAs, including let-7, and in this way plays a role in the heterochronic gene pathway during C. elegans development.
A Randomized-Controlled Crossover Trial of Mindfulness for Student Psychotherapists

Although research has documented a number of personal benefits for psychotherapists who practice mindfulness, the existing research examining the effects of psychotherapist mindfulness on client treatment outcomes has been mixed. The purpose of this study was to test whether a brief mindfulness training program for trainee psychotherapists could have a positive impact on psychotherapists’ ratings of their own state and trait mindfulness as well as on psychotherapy session outcomes. In this randomized-controlled crossover trial, 40 graduate student psychotherapists from 2 universities were assigned to either a mindfulness or a control group. Psychotherapists in the mindfulness group received a brief 5-week manualized mindfulness training program; those in the control group received the program after a 5-week no-contact period. Psychotherapists who received the mindfulness training showed improvements in state and trait mindfulness that were significantly greater than the changes seen in the control group. Psychotherapists also reported a significantly higher level of presence in treatment sessions after attending the training compared to the control condition; however, no differences in clients’ ratings of psychotherapist presence or session effectiveness were observed between the 2 conditions. Implications for psychotherapist training and mindfulness practice are discussed.
Fattening and Comparison Principle for Level-Set Equations of Mean Curvature Type

In this paper, we give several applications of the discrete game approach to partial differential equations (PDEs). We first present a rigorous game-theoretic proof of fattening phenomenon for motion by curvature with figure-eight-shaped initial curves without using parabolic PDE theory. The proof is based on a comparison between the game value and its inverse. Accompanied by the example of figure eight, our second result shows, for the stationary equation of mean curvature type in an arbitrary region $\Omega$, that fattening of positive curvature flow with initial surface $\partial\Omega$ causes loss of the weak comparison principle, which partially answers an open question posed by Kohn and Serfaty in 2006. In addition, we prove the existence of solutions of the stationary problem and its game approximation in the absence of comparison principles but under regularity conditions of the flow. The main difference between our games and those in other papers is that we take the domain perturbation into consideration.
Report Transfer Learning of Deep Convolutional Network on Twitter

This report aims at showing the capacity of transfering a deep neural network on char-level on massive dataset Twitter, using distant supervision. We showed that more data could help for the Stanford140 dataset. The best overal result observed is 84% of transfer learning for two sentiment polarity classes (positive-negative) from 16M emoticons subjective SESAMm dataset to small SemEval 2013 dataset. Other learning of three classes (with neutral) or nine classes based on emoticons (happy, laughing, kisswink, playful, sad, horror, shock, annoyed, hesitated) didn't show any advantages yet in the study.
PROBABILISTIC SEMANTIC ANALYSIS IN RESTRICTED DOMAINS

We present a probabilistic approach to semantic and pragmatic analysis in restricted domains and methods to improve the understanding performance. As framework we use the EVAR system, an automatic dialog system for answering queries on German Intercity train connections over the public telephone network. We introduce the statistical model extracting the semantic content from a word chain as well as our annotation scheme for the semantic of word chains for a specialized task which allows fast and easy annotation. The task of detecting the semantic contents of a word chain is described as the problem of assigning semantic attributes to words. The statistical framework we use has to deal with incomplete data estimation problems which are solved through applying the Expectation Maximization algorithm. The resulting iterative estimation formulas for the desired parameters are presented. The baseline experiment and the obtained recognition results prove the feasibility of our model. We present several methods that are able to support our probabilistic semantic analysis. We present experiments using categorial systems on the lexicon, different initializing methods for probabilistic parameters and combinations of these methods. The results show that the supporting power of the techniques is small when they are used individually but remarkable for the combination of them.
Word-of-Mouth Communication and Social Learning

This paper studies the way that word-of-mouth communication aggregates the information of individual agents. We find that the structure of the communication process determines whether all agents end up making identical choices, with less communication making this conformity more likely. Despite the players' naive decision rules and the stochastic decision environment, word-of-mouth communication may lead all players to adopt the action that is on average superior. These socially efficient outcomes tend to occur when each agent samples only a few others.
Image Classification by Labeled Data in Similar Topics

One of the most difficulties in image classification is the short availability of labeled images. However, in many cases, we can find a large amount of auxiliary labeled images to help the target classification. The auxiliary images are in similar topics of the target images. For example,donkey may be used as auxiliary data of horse, since they are quite similar with each other. In this paper, we propose to use similar but different labeled images as auxiliary data to help train image classifiers. An SVM-based classification algorithm is proposed to tackle our problem. Our algorithm first maximizes the margin between the decision boundary and the training instances, and then minimizes the penalty corresponding to misclassification. We conduct extensive experiments to demonstrate the effectiveness of our algorithm. The experimental results show that our algorithm can greatly improve the image classification performance by making use of similar labeled image as auxiliary data, against several state-of-the-art image classification algorithms.
Statistical Post-Editing of a Rule-Based Machine Translation System

Automatic post-editing (APE) systems aim at correcting the output of machine translation systems to produce better quality translations, i.e. produce translations can be manually post-edited with an increase in productivity. In this work, we present an APE system that uses statistical models to enhance a commercial rule-based machine translation (RBMT) system. In addition, a procedure for effortless human evaluation has been established. We have tested the APE system with two corpora of different complexity. For the Parliament corpus, we show that the APE system significantly complements and improves the RBMT system. Results for the Protocols corpus, although less conclusive, are promising as well. Finally, several possible sources of errors have been identified which will help develop future system enhancements.
ROSPECTIVE TEACHERS’ ACHIEVEMENT GOALS, LEARNING STRATEGIES AND ENGAGEMENT IN LESSONS: A MEDIATION ANALYSIS

The purpose of the present study is twofold. First, it is to examine the relationships between prospective teachers’ achievement goals, learning strategies, and engagement in lessons; and second, it is to investigate the mediating roles of prospective teachers’ learning strategies in the relationships between their achievement goals and engagement in lessons. An explanatory correlational design was adopted in the study. A total of 312 prospective teachers participated voluntarily in the study. The results demonstrated that the prospective teachers’ achievement goals, learning strategies, and engagement in lessons were significantly related to each other. The results also showed that the relationships between the prospective teachers’ achievement goals and engagement in lessons were significantly mediated by their learning strategies. Implications for teacher education and directions for future studies were also discussed in the study.