Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning the Relevance of Relevance and the Trouble with Truth: Evaluating Explanatory Relevance across Childhood

ABSTRACT In four experiments, we investigate how the ability to detect irrelevant explanations develops. In Experiments 1 and 2, 4- to 8-year-olds and adults rated different types of explanations about “what makes cars go” individually, in the absence of a direct contrast. Each explanation was true and relevant (e.g., “Cars have engines that turn gasoline into power”), true and irrelevant (e.g., “Cars have radios that play music”), or a false statement that would be relevant if it were true (e.g., “Cars have rockets that speed them up”). Participants of all ages spontaneously indicated that false explanations were less helpful than relevant explanations. However, there was a developmental shift for irrelevant explanations: 4-year-olds only detected irrelevant explanations that did not involve internal features of cars (e.g., “Cars have parking lots that they park in”). Crucially, this shift between age 4 and 5 cannot be explained by 4-year-olds’ lack of knowledge since 4-year-olds correctly indicated that relevant explanations were more helpful than irrelevant feature explanations when given a direct contrast in Experiment 3. These results are further clarified in Experiment 4, in which we provided a different explanatory goal (“where to find cars”) and found that even young children have a nuanced understanding of explanatory relevance that is sensitive to differing explanatory goals. Together, these four experiments suggest an early-emerging ability to understand relevance, but a shift between age 4 and 5 in the ability to spontaneously use this understanding when evaluating individual explanations in isolation.
Over the Top? Overpricing and Advertising Effectiveness

This study investigates whether the effectiveness of advertising is limited by overpricing of the advertised good. Using a proprietary database of residential property sales and listings, we find that 1) advertising is effective in increasing a good's sale hazard and transaction price and 2) advertising effectiveness is significantly reduced in the presence of overpricing. The decrease of advertising effectiveness with the extent of overpricing suggests that prospective buyers are able to identify significant overpricing. The reduction in advertising effectiveness is less pronounced for atypical properties and in areas with strong price growth. Our findings shed new light on the effectiveness of advertising relative to pricing aspects.
Support Vector Machines vs Multiplicative Neuron Model Neural Network in Prediction of Bank Failures

Support Vector Machines have been developed as an alternative for classification problems to the classical learning algorithms, such as Artificial Neural Network. Thanks to advantages of using kernel trick, having a global optimum and a simple geometric interpretation support vector machines are known as the best classifiers among others. Classical learning algorithms such as Neural Network suffers from local optima and overfitting. Moreover for Neural Network systems deciding on the number of hidden layers is a very important problem. In this study we used both Gaussian Kernel based Support Vector Machines and a single Multiplicative Neuron Model Neural Network and compared their prediction performances for financial failure. We focused on predicting the failure of the Turkish banking system using a longitudinal data set consisting of financial ratios. The financial ratios are used as independent variables whereas the dependent variable was the success of the banks. Classification measures were used to compare the modeling performances.
Optimization-based motion generation for multiped robots in contact scenarios

The introduction of legged robots into society has the potential to impact many aspects of our lives. The versatile morphology of robots with arms and legs, or multipeds, allows them to operate in a broad spectrum of environments. For example, legged robots could assist at construction sites or locomote in rocky terrain under persistent interaction with the environment through contact. These behaviors require algorithms that translate high level operator commands into movement and contact interaction with the environment, steering robots towards successful task completion. Traditional planning and control methods are limited by a core property of legged robots; they can fall. Control algorithms must respond quickly in order to keep a multiped in balance, and planners are required to respond to changes in the environment. This thesis presents a movement control framework designed to control complex contact interactions between a multiped robot and its environment. An optimal control based architecture is proposed that treats planning and control as one coherent optimization program. The lookahead capabilities of planners are combined with the reactiveness of feedback controllers in a multi-layer optimization architecture. An optimal control problem plans whole-body motion and contact forces over a lookahead while a fast feedback loop computes motor commands consistent with the current higher layer plan. At the core of our analysis we exploit the mathematical structure relating the robot’s center of mass to the contact forces between the robot and the environment, leading to a specialized algorithm combining planning and control. A trajectory optimization method is proposed to compute whole-body motion and corresponding contact force plans. It is decomposed into two sub-problems with reduced complexity and solved with a composition of specialized solvers. A reactive feedback controller tracks the high level plan using hierarchical inverse dynamics. Our control architecture successfully plans and controls a stepping task over complex terrain. Optimal control depends on an accurate robot model and often ignores real-world sensor noise and delay. Further, the short reaction time required to prevent legged robots from falling limits the amount of computation time permitted. Therefore, we demonstrate the validity of our approach and modeling assumptions through experimental validation on a torque controlled humanoid robot. An efficient implementation of our hierarchical inverse dynamics solver in a 1kHz control loop exhibits robust push rejection in a series of balancing experiments. The proposed controller successfully balances in a wide range of scenarios, including on a moving see-saw, on a sliding platform, and being pushed while standing on one foot and tracking a desired trajectory with the other.
People Analytics of Semantic Web Human Resource Résumés for Sustainable Talent Acquisition

The purpose of this study was to define a data science architecture for talent acquisition. The approach was to propose analytics that derive data. The originality of this paper consists in proposing an architecture to work within the process of obtaining semantically enriched data by using data science and Semantic Web technologies. We applied the proposed architecture and developed a case study-based prototype that uses analytics techniques for résumé data integrated with Linked Data technologies. We conducted a case study to identify skills by applying classification via regression, k-nearest neighbors (k-NN), random forest, naïve Bayes, support vector machine, and decision tree algorithms to résumé data that we previously described with terms from publicly available ontologies. We labeled data from résumés using terms from existing human resource ontologies. The main contribution is the extraction of skills from résumés and the mining of data that was previously described with the Semantic Web.
Communicating evolution with a Dynamic Evolutionary Map

Metaphors and visualizations are important for science communication, though they may have limitations. This paper describes the development and evaluation of a novel interactive visualization, the “Dynamic Evolutionary Map” (DEM), which communicates biological evolution using a non-standard metaphor. The DEM uses a map metaphor and interactivity to address conceptual limitations of traditional tree-based evolutionary representations. In a pilot evaluation biology novices used the DEM to answer questions about evolution. The results suggest that this visualization communicates some conceptual affordances differently than trees. Therefore, the described approach of building alternative visual metaphors for challenging concepts appears useful for science communication.
Cracking the Code of Effective Learning through Management of Learning Experiences.

This article is an attempt to explore why the efforts of universities fall short of accomplishing their objectives, and fail to improve the caliber of their graduates, as large numbers of them are unable to encounter real-life problems. It also probes the relations between four independent demographic variables (gender, age, experience, and specialization) and four dependent variables (role of faculty members, learning experiences focused upon, measures principally administered to achieve learning experiences, and the skills necessary to create synergy and momentum). A relevant online questionnaire is devised to survey the views of a sample of (176) faculty members, comprising (60.5%) of the faculty society at Philadelphia University/Jordan in 2015-2016. SPSS program is utilized for data processing. The article concludes that faculty members focus upon facilitating provision of information instead of achieving self-actualization. The farther objectives of learning experiences are from that, the more learners are subject to become incompetent.
Sentiment Analysis of Arabic Tweets in Sudanese Dialect

 Abstract — Sentiment analysis is the field of science that deals with extracting opinions embedded in human oral or written speech. In this paper we focus on sentiment analysis of Arabic tweets written using either Modern Standard Arabic or Sudanese dialectical Arabic. We have created our own lexicon which contain 2500 words and we have applied three different classifiers on the dataset namely; Support Vector Machine (SVM), Naive Bayes (NB) and K-Nearest Neighbor (K-NN), to classify the tweets based on its polarity into positive or negative. We evaluate our work by four different measures which are Precision, Recall, Accuracy and F-measure. The results show that, SVM achieved the best Recall, Accuracy and F-measure and it equals 95.1%, 76.5% and 84.4% respectively. While NB achieved best Precision and it equals to 85.1%.
Process mining techniques: an application to time management

In an environment people have to make sure that all of their work are completed within a given time in accordance with its quality. In order to achieve the real phenomenon of process mining one needs to understand all of these processes in a detailed manner. Personal Information and communication has always been a highlighting issue on internet but for now information and communication tools within factual life refers to their daily schedule, location analysis, environmental analysis and, more generally, social media applications support these systems which makes data available for data analysis generated through event logs, but also for process analysis which combines environmental and location analysis. Process mining can be used to exploit all these real live processes with the help of the event logs which are already available in those datasets through user censored data or may be user labeled data. These processes could be used to redesign a user’s flow and understand all these processes in a bit more detailed manner. In order to increase the quality of each of the processes that we go through our daily lives is to give a closer look to each of the processes and after analyzing them, one should make changes to get better results. On the contrarily, we applied process mining techniques on seven different subjects combined in a single dataset collected from Korea. Above all, the following paper comments on the efficiency of processes in the event logs referring to time management’s sphere of influence.
Combining Genetic Search into Neural Network Based Face Detection

: 1n this paper ， we investigate the idea of using Artificial Neural Network (ANN) and Genetic Algorithms (GAs) to detect upright ， frontal views of faces in complex gray-scale scenes. The first component of our system is a neural network that receives as input a 20x20 pixel region of the image ， and generates an output ranging from 0 to 1 ， signing the presence or absence of a face. The neural network is trained with many face examples to learn the concept of face. GA is employed to search efficiently the input image by extracting sub-windows from the input image. The sub-window is sent to the NN ， what is called the face . filter ， to see how c1 0sely it resembles a face. Fitness is given according to the output of the NN. Based on its fitn~ss the sub-window is retained by the GA in subsequent steps. Experimental results show that significant speed Li p can be achieved by applying the proposed approach.
Smart Contracts for Research Data Rights Management over the Ethereum Blockchain Network

ABSTRACT This article presents a new method for managing digital reuse rights of research data, which leverages technologies such as the blockchain and smart contracts. This allows, on one hand, the creation of a permanent record on the agreements between the authors of the data and the reusers, with the possibility of verifying compliance at any time, and on the other hand, a higher level of granularity on defining the conditions of reuse. A practical implementation of such a workflow using the Solidity smart contract language is included, along with a brief analysis over the Ethereum blockchain network.
Learning Cognitive Affordances for Objects from Natural Language Instruction

Affordance perception refers to the ability of an agent to extract meaning and usefulness of objects in its environment. Cognitive affordance is a richer notion that extends traditional aspects of object functionality and action possibilities by incorporating the influence of changing context, social norms, historical precedence, and uncertainty. This allows for an increased flexibility with which to reason about affordances in a situated manner. Existing work in cognitive affordances, while providing the theoretical basis for representation and inference, does not describe how they can be (a) learned, and (b) integrated and used with a robotic system. In this work, we describe, demonstrate and evaluate an integrated cognitive robotic architecture which can learn cognitive affordances for objects from natural language and immediately use this knowledge in a dialoguebased learning and instruction task.
Deep Learning Based Fast Multiuser Detection for Massive Machine-Type Communication

Massive machine-type communication (MTC) with sporadically transmitted small packets and low data rate requires new designs on the PHY and MAC layer with light transmission overhead. Compressive sensing based multiuser detection (CS-MUD) is designed to detect active users through random access with low overhead by exploiting sparsity, i.e., the nature of sporadic transmissions in MTC. However, the high computational complexity of conventional sparse reconstruction algorithms prohibits the implementation of CS-MUD in real communication systems. To overcome this drawback, in this paper, we propose a fast Deep learning based approach for CS-MUD in massive MTC systems. In particular, a novel block restrictive activation nonlinear unit, is proposed to capture the block sparse structure in wide-band wireless communication systems (or multi-antenna systems). Our simulation results show that the proposed approach outperforms various existing algorithms for CS-MUD and allows for ten-fold decrease of the computing time.
Avaliação de refluxo ultra-distal com pHmetria de múltiplos canais

RACIONAL: Displasia e adenocarcinoma esofagico surge em pacientes com esofago de Barrett submetidos a tratamento cirurgico (fundoplicatura) com pHmetria esofagica sem evidencia de acidez, o que sugere existir refluxo distal ao cateter de pHmetria convencional. OBJETIVO: Desenvolver metodologia para avaliar refluxo ultra-distal (1 cm acima da borda superior de esfincter inferior do esofago). METODO: Foram selecionados 11 pacientes com esofago de Barrett previamente submetidos a fundoplicatura a Nissen, sem sintomas de refluxo, com endoscopia e estudo contrastado de esofago sem sinais de recidiva. Foi realizada manometria esofagica para avaliar a localizacao e a extensao do esfincter esofagico inferior (EIE). Realizou-se entao pHmetria esofagica com quatro canais: canal A a 5 cm acima da borda superior do EIE; canal B a 1 cm acima; canal C intraesfincteriano; canal D intragastrico. Avaliou-se o escore de DeMeester no canal A. Comparou-se o numero de episodios de refluxo acido, o numero de episodios de refluxo prolongado e a fracao de tempo com pH<4,0 nos canais A e B. Comparou-se a fracao de tempo de pH<4,0 nos canais B e C. A fracao de tempo com pH<4,0 acima de 50% no canal D foi usada como parâmetro para nao migracao proximal do cateter. RESULTADOS: Houve aumento significativo do numero de episodios de refluxo e da fracao de tempo com pH<4,0 no canal B em relacao ao canal A. Houve reducao do tempo de pH<4,0 no canal B em comparacao ao canal C. Dois casos de adenocarcinoma esofagico foram diagnosticados nos pacientes do grupo estudado. CONCLUSOES: A regiao 1 cm acima da borda superior do EIE esta mais exposta a acidez do que a regiao 5 cm acima, embora em niveis reduzidos. A regiao 1 cm acima da borda superior do EIE esta menos exposta a acidez do que a regiao intraesfincteriana, demonstrando eficacia da fundoplicatura.
Measuring the Impact of Crowdsourcing Features on Mobile App User Engagement and Retention: A Randomized Field Experiment

The most commonly cited issues with mobile apps are low user engagement and retention levels. In this paper, we use a randomized control trial to test the efficacy of crowdsourcing on enhancing user engagement and retention in the context of mobile gaming apps. We examine two specific crowdsourcing features: the ability to submit content and the ability to access crowdsourced content. We find that the content submission significantly increases engagement and retention by reducing users’ hazard of ending a session by approximately 11% relative to the baseline and reducing the hazard of abandoning the game app by 14%. In contrast, giving users the ability to access crowdsourced content has no significant effect on engagement but has a positive effect on retention by reducing the hazard of abandoning the game app by 13%. Surprisingly, we find that the interaction effect of these two crowdsourced features is negative on user engagement. Individually, the submission feature manifests itself via empowering users to control their product use experience, whereas the access feature’s positive effect on retention is mediated by diversity and novelty of content. However, the two effects are not complementary. It turns out when these two features are given together, the empowerment enabled by one’s own submission is crowded out by others’ submissions, and this dominates the diversity benefit. Crowdsourcing features have heterogeneous impact on different user segments, with heavy users and users of longer tenure being more affected by the crowdsourcing features. This paper was accepted by Anandhi Bharadwaj, information systems.
Biochemical effects of drinking Terminalia catappa Linn. decoction in Wistar rats

The biochemical effects of drinking Terminalia catappa Linn. decoction in place of water using weanling wistar rat (Rattus norvegicus) models of both sexes was studied. In folklore, the decoction is taken as a medicine by sicklers. Some of the phytochemical and chemical constituents detected in the decoction included tannins, flavonoids, saponins, β-carotene, thiocyanates, cardiac glycosides, alkaloids, cyanogenic glycosides, vitamin C, ρ – hydroxybenzoic acid (ρ – HBA), alkaloids, catechins, free amino acids and monosaccharides. The decoction was acidic, pH 5.94 ± 0.01. It was administered to the rats in place of water, ad libitum. Test for its biochemical effects lasted for 35 days and was ingested at 18.3 mg/ml. Results of its effects on liver function parameters showed that most of the parameters were not significantly (p> 0.05) affected. However, it increased (p< 0.05) the alanine aminotransferase activity and serum total protein contents of the female rats and significantly reduced (p< 0.05) the serum total bilirubin levels of both male and female rats. It also significantly (p< 0.05) reduced the serum total cholesterol levels of the female rats and the serum LDL – cholesterol levels of both the male and female rats. The haematological indices were not significantly (p>0.05) altered. Ingestion of the decoction in place of water significantly (p< 0.05) increased the body weight gained, fluid and feed intakes and did not interfere with the nutrient adequacy of the feed by reducing the feed conversion ratios (FCR’s) of the test rats. In conclusion, the study established the safety of the decoction when drunk in place of water.
High-Power Ultrasonic Synthesis and Magnetic-Field-Assisted Arrangement of Nanosized Crystallites of Cobalt-Containing Layered Double Hydroxides

High-quality stoichiometric Co2Al–NO3 and Co2Al–CO3 layered double hydroxides (LDHs) have been obtained by precipitation followed by anion exchange, both high-power-sonication assisted. Application of high-power ultrasound has been demonstrated to result in a considerable acceleration of the crystallization process and the anion-exchange reaction. Two independent approaches were used to form bulk and 2-D samples of Co2Al–NO3 with the oriented crystallites, namely uniaxial pressing of deposits from sonicated LDH slurries and magnetic field-assisted arrangement of LDH crystallites precipitating on glass substrates. A convenient way of preparation of semi-transparent compacts with relatively big blocks of oriented crystallites have been demonstrated. Thin dense transparent films of highly-ordered crystallites of Co2Al–NO3 LDH have been produced and characterized.
Automatic Parameter Switching of Noise Reduction for Speech Recognition

Noise reduction has been extensively studied to prevent the accuracy of automatic speech recognition (ASR) being severely degraded due to various noise sources observed in practical use. The effect of noise reduction is usually dependent on the parameters used in the noise-reduction method, which need to be tuned for different types of noise to achieve the best accuracy. We propose a method for automatically switching noise-reduction parameters in such a way that ASR accuracy is maximized. The proposed method assigns the most suitable parameter set to each speech sentence by measuring and grouping the characteristics of noise observed in each sentence. Experiments using speech sentences contaminated by various types of noise showed that the proposed method can reduce the word error rate of ASR.