Miracle-dz/UniFAR-FTUs huggingface dataset:
Age-related decline : Effects of video game training on behavioural and neuroimaging measures of attention and memory View project AGNES : Systems for successful ageing in a networked society View project

Resumen Background: Previous research has shown repetition priming for objects between vision and touch, environmental sounds and pictures in young and older adults. Methods: This preliminary study investigated whether repetition priming exists for edible stimuli and if it is preserved in healthy older adults and older adults with mild cognitive impairment (MCI) (Experiment 1). Experiment 2 investigated whether cross-modal repetition priming between taste and olfaction is preserved with age and cognitive impairment. Results: The results of Experiment 1 showed signifi cant repetition priming effects for edible stimuli presented to taste, but there was a signifi cant decrease in performance with age and cognitive decline. Experiment 2 showed cross-modal taste to olfactory priming in young adults and healthy older people, but the performance of older adults with MCI did not differ from zero. Again, identifi cation decreased signifi cantly in older adults and was absent in those with MCI. Conclusions: Implicit memory for stimuli presented to taste persists in healthy older adults and those with MCI, although their performance declined compared to young adults. The fi nding of cross-modal taste and olfactory priming suggests the connection between these two chemosensory perceptual modalities.
Finger-Based Personal Authentication : A Comparison of Feature-Extraction Methods Based on PCA , MDF and RD-LDA

In this paper, feature-extraction methods based on Principal Component Analysis (PCA), Most Discriminant Features (MDF), and Regularized-Direct Linear Discriminant Analysis (RD-LDA) are tested and compared in an experimental fingerbased personal authentication system. The system is multimodal and based on features extracted from eight regions of the hand: four fingerprints (the prints of the finger tips) and four digitprints (the prints of the fingers between the first and third phalanges). All of the regions are extracted from one-shot grey-level images of the palmar surface of four fingers of the right hand. The identification and verification experiments were conducted on a database consisting of 1840 finger images (184 people). The experiments showed that the best results were obtained with the RDLDA-based feature-extraction method − 99.98% correct identification for 920 tests and an Equal Error Rate (EER) of 0.01% for 64170 verification tests.
Towards Automatic Recognition of Fonts using Genetic Approach

: - An algorithm has been designed that models the outlines of handprinted and electronic fonts. By capturing the input through a scanning device, the bitmapped image is converted to a more flexible format by means of a mathematical description. This in turn reflects the input font through a series of non-uniform rational B-spline segments, pieced together via an acceptable level of continuity. An optimal solution is realised by the method being applied using a genetic approach. The algorithm has been tested on a number of symbols.
A Simple Wavelet-Estimation Approach for Well-Log to Seismic Tying

We propose a simple method to estimate the wavelet directly from the seismic records alone. The method is based on utilizing the strongest reflectors, analogous to iterative time-domain deconvolution often used in earthquake seismology. This method is efficient for calculation, robust with respect to noise, and provides a satisfactory accuracy in synthetic and real-data tests. The method is convenient for tying well logs to stacked seismic sections, which is illustrated by the data from the Weyburn Field, Saskatchewan.
A ROBUST METHOD FOR DETECTION OF OCCLUSAL POSITION

We have developed a system that enables dental prostheses to be designed on a computer. The occlusal position for the upper and lower jaw objects should be found when designing dental prostheses on our system. However, there is often great difficulty in manually finding this position. To reduce this difficulty, we considered finding it and occluding jaw objects automatically. In positioning upper and lower jaw objects, some regions, such as teethridges and sides of teeth, do not completely close on one another, even if these objects are occluded. The evaluation value for these regions is an error, and consequently finding the occlusal position requires robust assessment. In this paper, we propose a method of evaluation using a robust estimator, the M-estimator. Applying multi-resolution representation, we also attempt to improve runtime.
A Historical Syntax of English

Aimed at advanced students, this book discusses a number of approaches to charting the major developments in the syntax of English. It does not assume any knowledge of Old or Middle English or of formal syntax, although students should be familiar with traditional syntactic concepts such as verbs and nouns, subjects and objects, and linguistic concepts such as morphology and case. Bettelou Los draws on explanations from both formal and functional approaches to explore how syntactic changes are the product of the interaction of many internal and external factors.
Computational Surprise in Information Retrieval

The concept of surprise is central to human learning and development. However, compared to accuracy, surprise has received little attention in the IR community, yet it is an essential component of the information seeking process. This workshop brings together researchers and practitioners of IR to discuss the topic of computational surprise, to set a research agenda, and to examine how to build datasets for research into this fascinating topic. The themes in this workshop include discussion of what can be learned from some well-known surprise models in other fields, such as Bayesian surprise; how to evaluate surprise based on user experience; and how computational surprise is related to the newly emerging areas, such as fake news detection, computational contradiction, clickbait detection, etc.
Using Multi-Agent System for Code and Data Propagation

This work presents the concept of code and data propagation in a multi-agent system. First, the concepts of agent and multi-agent system are defined and examples are presented. Also arguments for using agent approach are given and potential benefits are listed. Afterwards the idea of code and data propagation is defined and explained. Then some examples of real solutions are given along with propagation algorithms, which depict the way of introducing the concept into real system solutions. Afterwards the code and data propagation in a multi-agent system is described, which is in many cases based on the object migration. Discussion about this concept ends with describing the types of agent migration and giving some examples of systems with agent migration. Then three multi-agent environments are described and one is chosen to be the basis of the implemented application. Second part of work starts with description of systems principal objective, which is the distributed calculating of prime numbers. At the beginning, agents of the system are presented, and then the main system processes are depicted in detail. All algorithms are shown in sequence diagrams, which point all asynchronisms in the system. Afterwards migration phase is described with all migration types and algorithms. The experiments are conducted in two environments: home and university. The main aim is to find optimal configurations for both environments. The conclusion from this work is that introducing code and data propagation to a multiagent system in a form of agent migration in a heterogeneous network could considerably decrease the execution time. Moreover based on the efficiency vector of computers participating in the experiment there is a possibility to set a task distribution, which is close to optimal, without searching for optimal configuration every time when running the experiment.
On Not Reinventing the Wheel: Need We See the Governance of Research in Neuroscience as Somehow Special?

BAC is, at the time of writing, actively considering the implications of new technologies for neuroscience research within an established framework of ethics governance in Singapore, and what if any consequential changes to this framework might be needed. This therefore seems an opportune moment to consider some potential ethical issues in neuroscience research. This article acknowledges and briefly describes the ethical framework that the BAC fuses, but is written independently and not as representing the BAC or its position on these issues. Neuroscience attracts interest because the brain is the organ of the mind and the seat of consciousness. Accepting, for the moment, the inherent dualism behind this formulation, together with a naturalistic implication that the undisturbed brain somehow reflects our true selves, it seems that anything that might seem to risk or alter the brain, risks altering ourselves. Whether or not this accurately captures an important concern, it will do as the starting point for an exploration of whether some kind of neuroscience exceptionalism is justified. By neuroscience exceptionalism, I mean simply the proposition that neuroscience, by virtue of its subject matter, requires special or different treatment when it comes to the ethical principles that are entailed in good ethics governance, or their application.
Investigating User Perception of Gender Bias in Image Search: The Role of Sexism

There is growing evidence that search engines produce results that are socially biased, reinforcing a view of the world that aligns with prevalent social stereotypes. One means to promote greater transparency of search algorithms - which are typically complex and proprietary - is to raise user awareness of biased result sets. However, to date, little is known concerning how users perceive bias in search results, and the degree to which their perceptions differ and/or might be predicted based on user attributes. One particular area of search that has recently gained attention, and forms the focus of this study, is image retrieval and gender bias. We conduct a controlled experiment via crowdsourcing using participants recruited from three countries to measure the extent to which workers perceive a given image results set to be subjective or objective. Demographic information about the workers, along with measures of sexism, are gathered and analysed to investigate whether (gender) biases in the image search results can be detected. Amongst other findings, the results confirm that sexist people are less likely to detect and report gender biases in image search results.
Large-Scale Image Retrieval with Elasticsearch

Content-Based Image Retrieval in large archives through the use of visual features has become a very attractive research topic in recent years. The cause of this strong impulse in this area of research is certainly to be attributed to the use of Convolutional Neural Network (CNN) activations as features and their outstanding performance. However, practically all the available image retrieval systems are implemented in main memory, limiting their applicability and preventing their usage in big-data applications. In this paper, we propose to transform CNN features into textual representations and index them with the well-known full-text retrieval engine Elasticsearch. We validate our approach on a novel CNN feature, namely Regional Maximum Activations of Convolutions. A preliminary experimental evaluation, conducted on the standard benchmark INRIA Holidays, shows the effectiveness and efficiency of the proposed approach and how it compares to state-of-the-art main-memory indexes.
Optimal core wrapper width selection and SOC test scheduling based on 3-D bin packing algorithm

This paper presents a method to consider a given SOC with pin and peak power constraints, and simultaneously (1) determine an optimal wrapper width for each core, (2) allocate SOC pins to cores and (3) schedule core tests to minimize the test completion time. For the first time the stated problem is formulated as a restricted 3 dimensional bin-packing problem and a heuristic to determine an optimal solution is proposed.
PDA+LDPC iterative detector for cooperative CDMA networks

The cooperative CDMA system proposed in this paper performs two phases of transmission. In phase I, each user node transmits its own information which is received by the base station (BS) as well as the other user nodes; in phase II, each partner node relays a processed version of the information received in the previous phase. Two iterative detectors are developed at the B S to combat multiple access interference (MAI). The first one named as PDA+LDPC-I performs detection just in the latter phase. The second one named as PDA+LDPC-II operates in both phases. During the latter phase, it firstly eliminates the signals corresponding to the users which were correctly decoded in the former phase, then performs detection on the resultant signals. Simulation results demonstrate that PDA+LDPC-II outperforms PDA+LDPC-I.
Design and Evaluation of Query Auto Completion Mechanisms

Query Auto Completion (QAC) systems present a list of completions to a partial query entered by the user. The completions extend the partial query and are updated with subsequent modifications made to the partial query. The partial query, or more generally, the query pattern is matched against a target string collection to retrieve a candidate set of strings. A scoring function is applied to the strings in the candidate set to choose a subset of them which are then presented in a specific order as the final completions. Krishnan et al. [8] define a set of QAC modes based on how a query pattern is matched with the target string collection. Each QAC mode produces distinct candidate sets and results in different completions being presented. A taxonomy of QAC modes will help us better understand the effects of match functions, desirable characteristics of an effective QAC method and their applicability to specific scenarios. The taxonomy proposed by Krishnan et al. [8] can be further refined to multiple dimensions such as error tolerance, phrase completions (without considering the order of the tokens), and completions on sub-tokens (complete just the final token vs complete each token). From an implementation aspect, each QAC mode needs specific algorithmic strategies and index structures. We measure the computational efficiency of different implementation strategies in terms of index memory, index construction time, querying and update time. For instance, a trie [1, 6, 7] is a commonly used data structure for prefix lookup, denoted “Mode 1". A different querying mode suggested by Bast andWeber [2] splits the pattern P based on white spaces and presents completions containing tokens having a prefix match with each query tokens. We call this Mode 2. Typical implementations for this mode (or its variants) use an inverted index [2, 5] to map the tokens to the documents they appear in and computes document list intersections for tokens from query pattern. If a pattern match is performed on the tokens instead of prefix match (as in Mode 2), we get “Mode 3”. A suffix tree or suffix
Translating Representations of Knowledge Graphs with Neighbors

Knowledge graph completion is a critical issue because many applications benefit from their structural and rich resources. In this paper, we propose a method named TransN, which consid- ers the dependencies between triples and incorporates neighbor information dynamically. In experiments, we evaluate our model by link prediction and also conduct several qualitative analyses to prove effectiveness. Experimental results show that our model could integrate neighbor information effectively and outperform state-of-the-art models.
Attention-driven Factor Model for Explainable Personalized Recommendation

Latent Factor Models (LFMs) based on Collaborative Filtering (CF) have been widely applied in many recommendation systems, due to their good performance of prediction accuracy. In addition to users' ratings, auxiliary information such as item features is often used to improve performance, especially when ratings are very sparse. To the best of our knowledge, most existing LFMs integrate different item features in the same way for all users. Nevertheless, the attention on different item attributes varies a lot from user to user. For personalized recommendation, it is valuable to know what feature of an item a user cares most about. Besides, the latent vectors used to represent users or items in LFMs have few explicit meanings, which makes it difficult to explain why an item is recommended to a specific user. In this work, we propose the Attention-driven Factor Model (AFM), which can not only integrate item features driven by users' attention but also help answer this "why". To estimate users' attention distributions on different item features, we propose the Gated Attention Units (GAUs) for AFM. The GAUs make it possible to let the latent factors "talk", by generating user attention distributions from user latent vectors. With users' attention distributions, we can tune the weights of item features for different users. Moreover, users' attention distributions can also serve as explanations for our recommendations. Experiments on several real-world datasets demonstrate the advantages of AFM (using GAUs) over competitive baseline algorithms on rating prediction.
Assessment of a multi-agent mixed-integer optimization algorithm for battery scheduling

In the present paper, the problem of scheduling populations of energy storage systems including asymmetric charging and discharging efficiencies is formulated as a mixed integer program. The problem is solved using both a novel distributed local mixed integer solution scheme and a global mixed integer solver. We draw upon a detailed numerical comparison of both methods via a simulation example built using real PV generation data consisting of 54 energy storage systems over a horizon of 48 time steps leading to 2640 integer variables. Our results indicate that the distributed local solution method delivers values comparable to the centralized global solver but in significantly reduced time.
A Dataset and an Examination of Identifying Passages for Due Diligence

We present and formalize the due diligence problem, where lawyers extract data from legal documents to assess risk in a potential merger or acquisition, as an information retrieval task. Furthermore, we describe the creation and annotation of a document collection for the due diligence problem that will foster research in this area. This dataset comprises 50 topics over 4,412 documents and ~15 million sentences and is a subset of our own internal training data. Using this dataset, we present what we have found to be the state of the art for information extraction in the due diligence problem. In particular, we find that when treating documents as sequences of labelled and unlabelled sentences, Conditional Random Fields significantly and substantially outperform other techniques for sequence-based (Hidden Markov Models) and non-sequence based machine learning (logistic regression). Included in this is an analysis of what we perceive to be the major failure cases when extraction is performed based upon sentence labels.
Detecting aliasing artifacts in image sequences using deep neural networks

In this short paper we present a machine learning approach to detect visual artifacts in rendered image sequences. Specifically, we train a deep neural network using example aliased and antialiased image sequences exported from a real-time renderer. The trained network learns to identify and locate aliasing artifacts in an input sequence, without comparing it against a ground truth. Thus, it is useful as a fully automated tool for evaluating image quality. We demonstrate the effectiveness of our approach in detecting aliasing in several rendered sequences. The trained network correctly predicts aliasing in 64 x 64 x 4 animated sequences with more than 90% accuracy for images it hasn't seen before. The output of our network is a single scalar between 0 and 1, which is usable as a quality metric for aliasing. It follows the same trend as (1-SSIM) for images with increasing sample counts.
Are we on the Right Track?: An Examination of Information Retrieval Methodologies

The unpredictability of user behavior and the need for effectiveness make it difficult to define a suitable research methodology for Information Retrieval (IR). In order to tackle this challenge, we categorize existing IR methodologies along two dimensions: (1) empirical vs. theoretical, and (2) top-down vs. bottom-up. The strengths and drawbacks of the resulting categories are characterized according to 6 desirable aspects. The analysis suggests that different methodologies are complementary and therefore, equally necessary. The categorization of the 167 full papers published in the last SIGIR (2016 and 2017) and ICTIR (2017) conferences suggest that most of existing work is empirical bottom-up, suggesting lack of some desirable aspects. With the hope of improving IR research practice, we propose a general methodology for IR that integrates the strengths of existing research methods.
Improving complex task performance using a sequence of simple practice tasks

Online coding tools are an increasingly common feature of programming courses, providing students with rapid feedback and flexible practice opportunities and providing instructors with useful analytics. However, little research has explored the complexity of online exercises provided to students and the order in which students are exposed to new ideas. In this paper, we investigate the benefits of using a short sequence of practice exercises, each targeting a distinct topic, prior to having students solve a goal task that combines the concepts. As expected, we find students solve the goal task with fewer errors and in less time after completing the practice tasks. However, we also find that the practice tasks reduce the likelihood of students delaying work on the goal task, and these effects are particularly large for less-experienced students.
The Effects of Manipulating Task Determinability on Search Behaviors and Outcomes

An important area of IR research involves understanding how task characteristics influence search behaviors and outcomes. Task complexity is one characteristic that has received considerable attention. One view of task complexity is through the lens of a priori determinability -- the level of uncertainty about task outcomes and processes experienced by the searcher. In this work, we manipulated the determinability of comparative tasks. Our task manipulation involved modifying the scope of the task by specifying exact items and/or exact (objective or subjective) dimensions to consider as part of the task. This paper reports on a within-subject study (N=144) where we investigated how our task manipulation influenced participants' perceptions, levels of engagement, search effort, and choice of search strategies. Our results suggest a complex relationship between task scope, determinability, and different outcome measures. Our most open-ended tasks were perceived to have low determinability (high uncertainty), but were the least challenging for participants due to satisficing. Furthermore, narrowing the scope of tasks by specifying items had a different effect than by specifying dimensions. Specifying items increased the task determinability (lower uncertainty) and made the task easier, while specifying dimensions did not increase the task determinability and made the task more challenging. A qualitative analysis of participants' queries suggests that searching for dimensions is more challenging than for items. Finally, we observed subtle differences between objective and subjective dimensions. We discuss implications for the design of IIR studies and tools to support users.