Miracle-dz/UniFAR-FTUs huggingface dataset:
The Future of Innovation Coaching in Product Engineering: A Systematic Approach to Deriving the Future Competence Profile and Its Development Through Strategic Potential Identification

Innovation Coaches assist organizations in harnessing their innovation potential in order to operate successfully in a rapidly changing and challenging environment. Especially in the context of agile approaches and distributed development projects, the approach of innovation coaching is a valuable contribution to the successful accomplishment of innovation projects due to a process-oriented support. This approach will face a series of new challenges in the future. Development leaps in digital media such as Virtualand Augmented Reality, Machine Learning and speech recognition create new challenges, but also new opportunities in in product development collaboration. Uncertain are the future competence requirements of an Innovation Coach and the corresponding impact on academic education. This paper presents the research results, which are based on qualitative studies as well as empirical investigations, conducted in an innovation project with 48 participants. The main responsibilities, tasks as well as key competences of the existing Innovation Coaching approach could be derived and applied in a profile. Furthermore, the application of a future scenario and a trend based approach leads to a derivation of future scenarios which implies not only required competences of an Innovation Coach, but also a development and training roadmap for the education of future Innovation Coaches.
Comparing machine learning clustering with latent class analysis on cancer symptoms' data

Symptom Cluster Research is a major topic in Cancer Symptom Science. In spite of the several statistical and clinical approaches in this domain, there is not a consensus on which method performs better. Identifying a generally accepted analytical method is important in order to be able to utilize and process all the available data. In this paper we report a secondary analysis on cancer symptom data, comparing the performance of five Machine Learning (ML) clustering algorithms in doing so. Based on how well they separate specific subsets of symptom measurements we select the best of them and proceed to compare its performance with the Latent Class Analysis (LCA) method. This analysis is a part of an ongoing study for identifying suitable Machine Learning algorithms to analyse and predict cancer symptoms in cancer treatment.
On the Security Effectiveness of Cryptographic Protocols 1 On the Security Effectiveness of Cryptographic Protocols

We introduce the notion of security effectiveness, illustrate its use in the context of cryptographic protocol analysis, and argue that it requires analysis of protocol property dependencies. We provide examples to show that, without dependency analysis, the use of some logics for cryptographic protocol analysis yields results that are inconsistent or unrealistic in practice. We identify several types of property dependencies whose use in protocol analysis methods can yield realistic analyses.
FER-1, an enhancer of the ferritin H gene and a target of E1A-mediated transcriptional repression

Ferritin, the major intracellular iron storage protein of eucaryotic cells, is regulated during inflammation and malignancy. We previously reported that transcription of the H subunit of ferritin (ferritin H) is negatively regulated by the adenovirus E1A oncogene in mouse NIH 3T3 fibroblasts (Y. Tsuji, E. Kwak, T. Saika, S. V. Torti, and F. M. Torti, J. Biol. Chem. 268:7270-7275, 1993). To elucidate the mechanism of transcriptional repression of the ferritin H gene by E1A, a series of deletions in the 5' flanking region of the mouse ferritin H gene were constructed, fused to the chloramphenicol acetyltransferase (CAT) gene, and transiently cotransfected into NIH 3T3 cells with an E1A expression plasmid. The results indicate that the E1A-responsive region is located approximately 4.1 kb 5' to the transcription initiation site of the ferritin H gene. Further analyses revealed that a 37-bp region, termed FER-1, is the target of E1A-mediated repression. This region also serves as an enhancer, augmenting ferritin H transcription independently of position and orientation. FER-1 was dissected into two component elements, i.e., a 22-bp dyad symmetry element and a 7-bp AP1-like sequence. Insertion of these DNA sequences into a ferritin H-CAT chimeric gene lacking an E1A-responsive region indicated that (i) the 22-bp dyad symmetry sequence by itself has no enhancer activity, (ii) the AP1-like sequence has moderate enhancer activity which is repressed by E1A, and (iii) the combination of the dyad symmetry element and the AP1-like sequence is required for maximal enhancer activity and repression by E1A. Gel retardation assays and cotransfection experiments with c-fos and c-jun expression vectors suggested that members of the Fos and Jun families bind to the AP1-like element of FER-1 and contribute to its regulation. In addition, gel retardation assays showed that E1A reduces the ability of nuclear proteins to bind to the AP1-like sequence without affecting the levels of nuclear factors that recognize the 22-bp dyad symmetry element. Taken together, these results demonstrate that FER-1 serves as both an enhancer of ferritin H transcription and a target for E1A-mediated repression.
Surface Tension Directed Fluidic Self-Assembly of Semiconductor Chips across Length Scales and Material Boundaries

This publication provides an overview and discusses some challenges of surface tension directed fluidic self-assembly of semiconductor chips which are transported in a liquid medium. The discussion is limited to surface tension directed self-assembly where the capture, alignment, and electrical connection process is driven by the surface free energy of molten solder bumps where the authors have made a contribution. The general context is to develop a massively parallel and scalable assembly process to overcome some of the limitations of current robotic pick and place and serial wire bonding concepts. The following parts will be discussed: (2) Single-step assembly of LED arrays containing a repetition of a single component type; (3) Multi-step assembly of more than one component type adding a sequence and geometrical shape confinement to the basic concept to build more complex structures; demonstrators contain (3.1) self-packaging surface mount devices, and (3.2) multi-chip assemblies with unique angular orientation. Subsequently, measures are discussed (4) to enable the assembly of microscopic chips (10 μm–1 mm); a different transport method is introduced; demonstrators include the assembly of photovoltaic modules containing microscopic silicon tiles. Finally, (5) the extension to enable large area assembly is presented; a first reel-to-reel assembly machine is realized; the machine is applied to the field of solid state lighting and the emerging field of stretchable electronics which requires the assembly and electrical connection of semiconductor devices over exceedingly large area substrates.
Building a Corpus for Palestinian Arabic: a Preliminary Study

This paper presents preliminary results in building an annotated corpus of the Palestinian Arabic dialect. The corpus consists of about 43K words, stemming from diverse resources. The paper discusses some linguistic facts about the Palestinian dialect, compared with the Modern Standard Arabic, especially in terms of morphological, orthographic, and lexical variations, and suggests some directions to resolve the challenges these differences pose to the annotation goal. Furthermore, we present two pilot studies that investigate whether existing tools for processing Modern Standard Arabic and Egyptian Arabic can be used to speed up the annotation process of our Palestinian Arabic corpus.
Multiple informant agreement on the anxiety disorders interview schedule in youth with autism spectrum disorders.

OBJECTIVE The purpose of this study was to examine child, parent, and clinician's consensus agreement on the Anxiety Disorders Interview Schedule, Child and Parent versions (ADIS-C/P) in a sample of children and adolescents with autism spectrum disorders (ASD). METHOD Youth with ASD (n=85; age range=7-17 years) and their parents were each administered the ADIS-C/P by a trained clinician. Consensus diagnoses were determined in a clinical conference using best estimate procedures that incorporated all available information. RESULTS Children and youth with ASD diagnoses generally showed poor diagnostic agreement with parents and clinical consensus, whereas parents showed good-to-excellent diagnostic agreement with clinical consensus diagnoses. Diagnostic agreement between parents and consensus was moderated by the specific ASD diagnosis. Otherwise, the pattern of relationships did not systematically differ as a function of age or externalizing comorbidity. CONCLUSIONS These data suggest that parent and youth agreement regarding the presence of clinical levels of anxiety is markedly poor among youth with ASD. Additionally, clinicians are likely to base their diagnostic impressions on parent report, placing minimal emphasis on child report.
Striatal FoxP2 Is Actively Regulated during Songbird Sensorimotor Learning

Background Mutations in the FOXP2 transcription factor lead to language disorders with developmental onset. Accompanying structural abnormalities in cortico-striatal circuitry indicate that at least a portion of the behavioral phenotype is due to organizational deficits. We previously found parallel FoxP2 expression patterns in human and songbird cortico/pallio-striatal circuits important for learned vocalizations, suggesting that FoxP2's function in birdsong may generalize to speech. Methodology/Principal Findings We used zebra finches to address the question of whether FoxP2 is additionally important in the post-organizational function of these circuits. In both humans and songbirds, vocal learning depends on auditory guidance to achieve and maintain optimal vocal output. We tested whether deafening prior to or during the sensorimotor phase of song learning disrupted FoxP2 expression in song circuitry. As expected, the songs of deafened juveniles were abnormal, however basal FoxP2 levels were unaffected. In contrast, when hearing or deaf juveniles sang for two hours in the morning, FoxP2 was acutely down-regulated in the striatal song nucleus, area X. The extent of down-regulation was similar between hearing and deaf birds. Interestingly, levels of FoxP2 and singing were correlated only in hearing birds. Conclusions/Significance Hearing appears to link FoxP2 levels to the amount of vocal practice. As juvenile birds spent more time practicing than did adults, their FoxP2 levels are likely to be low more often. Behaviorally-driven reductions in the mRNA encoding this transcription factor could ultimately affect downstream molecules that function in vocal exploration, especially during sensorimotor learning.
A Machine Learning Approach to Extract Temporal Information from Texts in Swedish and Generate Animated 3D Scenes

Carsim is a program that automatically converts narratives into 3D scenes. Carsim considers authentic texts describing road accidents, generally collected from web sites of Swedish newspapers or transcribed from hand-written accounts by victims of accidents. One of the program’s key features is that it animates the generated scene to visualize events. To create a consistent animation, Carsim extracts the participants mentioned in a text and identifies what they do. In this paper, we focus on the extraction of temporal relations between actions. We first describe how we detect time expressions and events. We then present a machine learning technique to order the sequence of events identified in the narratives. We finally report the results we obtained. (Less)
Shallow Semantic Parsing for Spoken Language Understanding

Most Spoken Dialog Systems are based on speech grammars and frame/slot semantics. The semantic descriptions of input utterances are usually defined ad-hoc with no ability to generalize beyond the target application domain or to learn from annotated corpora. The approach we propose in this paper exploits machine learning of frame semantics, borrowing its theoretical model from computational linguistics. While traditional automatic Semantic Role Labeling approaches on written texts may not perform as well on spoken dialogs, we show successful experiments on such porting. Hence, we design and evaluate automatic FrameNet-based parsers both for English written texts and for Italian dialog utterances. The results show that disfluencies of dialog data do not severely hurt performance. Also, a small set of FrameNet-like manual annotations is enough for realizing accurate Semantic Role Labeling on the target domains of typical Dialog Systems.
Forecasting Production Quantity by Integrating Time Series Forecast Technologies and Artificial Intelligence Methods

This study integrates artificial neural network (ANN) and fuzzy neural network (FNN) methods with the traditional time series technologies to improve the forecast accuracy. An Integrated Moving ANN (IMA) method and an Integrated Moving FNN (IMF) method are proposed by integrating some affect factors for order quantity and past period of time of quantity data for moving average method through AI method. The proposed methods are applied to help a massage chair part company to assess order quantity. The results indicate that the proposed integrated methods indeed can reduce forecast errors.
CONSORF: a consensus prediction system for prokaryotic coding sequences

CONSORF is a fully automatic high-accuracy identification system that provides consensus prokaryotic CDS information. It first predicts the CDSs supported by consensus alignments. The alignments are derived from multiple genome-to-proteome comparisons with other prokaryotes using the FASTX program. Then, it fills the empty genomic regions with the CDSs supported by consensus ab initio predictions. From those consensus results, CONSORF provides prediction reliability scores, predicted frame-shifts, alternative start sites and best pair-wise match information against other prokaryotes. These results are easily accessed from a website.
Computing aspects of monitoring walking disorder using body sensor network and neural network

Here, it is proposed to monitor walking disorder of any patient with the help of wireless three dimensional (3D) accelerometer based body sensors and its networks. We gather Ground Truth Data from the sensors, filter it, boost up it when required, then collect some important features and compare with the features of run time data using different algorithms developed by us. Where, for matching the run time features, we use supervised learning method of back propagation neural network. After gathering data, a prototype model of computation is developed which may be used in any motion disorder of any subjects like: patients, athletes, pilots and astronauts. The contribution of this paper is focused on to develop a model of computational processes required to monitor activity recognition system. The computing model developed is validated working over different walking motion disorders of different subjects and then discussed how this model can be applied in an organization to provide internet based online patient's information services with the help of Feature Server and Local Server connected by wireless radio link of Personal Computing Devices like mobile phone, PDA, laptop etc.
A cloud robotics approach towards dialogue-oriented robot speech

Robot utterances generally sound monotonous, unnatural and unfriendly because their Text-to-Speech systems are not optimized for communication but for text reading. Here, we present a non-monologue speech synthesis for robots. The key novelty lies in speech synthesis based on Hidden Markov models (HMMs) using a non-monologue corpus: we collected a speech corpus in a non-monologue style in which two professional voice talents read scripted dialogues, and HMMs were then trained with the corpus and used for speech synthesis. We conducted experiments in which the proposed method was evaluated by 24 subjects in three scenarios: text reading, dialogue and domestic service robot (DSR) scenarios. In the DSR scenario, we used a physical robot and compared our proposed method with a baseline method using the standard Mean Opinion Score criterion. Our experimental results showed that our proposed method’s performance was (1) at the same level as the baseline method in the text-reading scenario and (2) exceeded it in the DSR scenario. We deployed our proposed system as a cloud-based speech synthesis service so that it can be used without any cost. Graphical Abstract
On the method of obtaining indoor positioning information for realization advanced geospatial information society

To obtain indoor positioning information by GPS satellite is difficult now. The Japanese Government executed a new law NSDI (National Spatial Data Infrastructure) for a spatial information society on May 30, 2007. In this law, everybody can know positional information in real time anytime and anywhere[5]. In addition, it is necessary to keep satellite positioning for realization of seamless positioning, and to promote ubiquitous network technology [1] [4]. However, the technology has not been established yet. Therefore, Integrated Circuit Tag (IC tag) was used where GPS signals could not be received to obtain information on the absolute position. The experiment was conducted to verify the reading rate of IC tag on different types and conditions. An experiment was conducted to show position information to obtain seamlessly GPS positioning and IC tag on the GIS.
Probabilistic diagnosability of hybrid systems

The model-based fault diagnosability analysis is concerned with the timely detection and isolation of faults by using the system model and observations of the system output. In this paper, we propose the (δd, δm, α)-diagnosability notion for hybrid systems with probabilistic reset, where the faults are diagnosed by observing the timed event sequences. We also present an approach for the analysis of such diagnosability. The (δd, δm, α)-diagnosability notion characterizes the worst-case probability α of detecting and isolating faults within the maximum delay δd since their first occurrence, given the measurement uncertainty δm in observing the time intervals between observed events. We present a method of system abstraction, and prove a quantitative relation between the (δd, δm, α)-diagnosability of the original system and the abstraction. The abstraction has only finitely many trajectories that extend to the end of the time horizon of interest, which allows us to practically calculate the diagnosability and construct the diagnoser.
Unembedding domain-specific languages

Higher-order abstract syntax provides a convenient way of embedding domain-specific languages, but is awkward to analyse and manipulate directly. We explore the boundaries of higher-order abstract syntax. Our key tool is the unembedding of embedded terms as de Bruijn terms, enabling intensional analysis. As part of our solution we present techniques for separating the definition of an embedded program from its interpretation, giving modular extensions of the embedded language, and different ways to encode the types of the embedded language.
Detection and rapid differentiation of human enteroviruses following genomic amplification

By employing a nested PCR (n-PCR) with specific primers derived from the 5' nontranslated and consensus region of the human enterovirus genome, we detected enterovirus RNA from 32 different serotypes of prototypic strains. A specific 297-bp fragment was amplified by this method from all of these strains. Not only was the method highly sensitive, detecting enterovirus RNA extracted from 0.01 50% tissue culture infective dose/50 microl (which is more sensitive than our current routine method of enterovirus diagnosis, based on the virus isolation and serotypic neutralization), but it was also relatively rapid. By using this technology, we also detected enterovirus RNA in uncultured specimens (including throat swabs and stools) from patients with respiratory illness and acute flaccid paralysis syndrome. This method enabled us to rapidly and directly distinguish enterovirus-infected specimens from nonenterovirus specimens in laboratory diagnosis. Furthermore, restriction fragment length polymorphism was assessed as an alternative means of differentiating various serotypes of prototypical enteroviruses. Fourteen of 16 human enterovirus-infected specimens exhibited restriction patterns identical to those of the corresponding prototypes.
Involvement of Autophagic Pathway in the Progression of Retinal Degeneration in a Mouse Model of Diabetes

The notion that diabetic retinopathy (DR) is essentially a micro-vascular disease has been recently challenged by studies reporting that vascular changes are preceded by signs of damage and loss of retinal neurons. As to the mode by which neuronal death occurs, the evidence that apoptosis is the main cause of neuronal loss is far from compelling. The objective of this study was to investigate these controversies in a mouse model of streptozotocin (STZ) induced diabetes. Starting from 8 weeks after diabetes induction there was loss of rod but not of cone photoreceptors, together with reduced thickness of the outer and inner synaptic layers. Correspondingly, rhodopsin expression was downregulated and the scotopic electroretinogram (ERG) is suppressed. In contrast, cone opsin expression and photopic ERG response were not affected. Suppression of the scotopic ERG preceded morphological changes as well as any detectable sign of vascular alteration. Only sparse apoptotic figures were detected by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay and glia was not activated. The physiological autophagy flow was altered instead, as seen by increased LC3 immunostaining at the level of outer plexiform layer (OPL) and upregulation of the autophagic proteins Beclin-1 and Atg5. Collectively, our results show that the streptozotocin induced DR in mouse initiates with a functional loss of the rod visual pathway. The pathogenic pathways leading to cell death develop with the initial dysregulation of autophagy well before the appearance of signs of vascular damage and without strong involvement of apoptosis.
Differential familial liability of panic disorder and agoraphobia

To examine the familial liability of panic disorder (PD) and agoraphobia (AG) in a community sample, namely the effect of parental PD and AG on the offspring's risk to develop either or both conditions in adolescence or adulthood. A representative community sample of N=3,021 adolescents and young adults aged 14–24 years at baseline was followed up over a period of 10 years in up to four waves. Family information was assessed by either direct interviews with at least one parent or by using subjects' family history information at either wave (N=3,014). Diagnoses and selected symptoms were assessed in both, parents and subjects, by using a standardized diagnostic interview (DSM‐IV M‐CIDI) with its respective family history module. (1) Parental panic attacks (PA), PD, and AG were all shown to be associated with an increased risk of offspring to also develop PA, PD, and AG. (2) Associations of parental PD were present irrespective of parental AG, whereas parental AG without PD was not associated with an increased offspring risk. (3) Outcome risk was particularly elevated in offspring of parents with PD+AG. (4) Parental PD or AG was not associated with an earlier age of onset of any syndrome in the offspring. We confirmed and expanded previous results from clinical samples that comorbid PD and AG aggregate in families. AG without PD is not familial, but it might enhance the familial transmission of PD. Depression and Anxiety 0:1–13, 2007. © 2007 Wiley‐Liss, Inc.