Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining line search and trust-region methods for -minimization

ABSTRACT This study presents a new trust-region algorithm to solve the -minimization problem with applications to compressed sensing (CS) and image deblurring that will be augmented with a shrinkage operation to produce a new iteration whenever an approximated solution of the trust-region subproblem lies within one and iterate is successful, simultaneously. Otherwise, a nonmonotone Armijo-type line search strategy incorporates with shrinkage technique, which includes a convex combination of the maximum function value of some preceding iterates and the current function value. Therefore, the proposed approach takes advantages of both the effective trust-region and nonmonotone Armijo-type line search with a shrinkage operation. It is believed that selecting an appropriate shrinkage parameter according to a new procedure can improve the efficiency of our algorithm. The global convergence and the R-linear convergence rate of the proposed approach are proved for which numerical results are also reported.
Optimize Hierarchical Softmax with Word Similarity Knowledge

Abstract—Hierarchical softmax is widely used to accelerate the training speed of neural language models and word embedding models. Traditionally, people believed that the hierarchical tree of words should be organized by the semantic meaning of words. However, Mikolov et al. showed that high quality word embeddings can also be trained by simply using the Huffman tree of words. To our knowledge, no work gives a theoretic analysis on how we should organize the hierarchical tree. In this paper, we try to answer this question theoretically by treating the tree structure as a parameter of the training objective function. As a result, we can show that the Huffman tree maximizes the (augmented) training function when word embeddings are random. Following this, we propose SemHuff, a new tree constructing scheme based on adjusting the Huffman tree with word similarity knowledge. Experiment results show that word embeddings trained with optimized hierarchical tree can give better results in various tasks.
On-Road Evaluation of In-vehicle Interface Characteristics and Their Effects on Performance of Visual Detection on the Road and Manual Entry

Objective: This study investigated the impact of in-vehicle interface characteristics on drivers’ multitasking performance measures relating to visual attention management, concerning the distraction potential of in-vehicle touchscreens. Background: Compared with physical controls providing drivers with naturalistic nonvisual cues, in-vehicle touchscreen interaction relies on vision to a greater extent, leading to more time with eyes off the road and concerns for safety. Little is known from existing research about the extent to which synthetic feedback of in-vehicle touchscreens support visual attention of multitasking drivers, while automakers are increasingly incorporating nondriving functions into in-vehicle touchscreens. Method: Twenty-nine participants drove an instrumented vehicle on a closed course and acknowledged visual probes obscured on the roadside, while performing a manual data entry task with input interfaces mounted on the center console. The interfaces differed by interface type, key feedback modality, and key size; the configuration of interface characteristics was the within-subject variable. The collected data include performance measures concerning visual detection and touchscreen interaction, in addition to perceived workload. Results: The addition of nonvisual feedback to touchscreen interaction significantly improved accuracy and promptness of visual detection. No significant difference was found between different sizes of touchscreen keys when synthetic nonvisual feedback was available. Given multisensory feedback, no measure showed a difference between touchscreen conditions and a physical keypad. Conclusion: The provision of synthetic nonvisual feedback to touchscreen interaction can support visual attention and enhance multitasking performance in driving. Application: This study can inform in-vehicle interface designers and policy makers concerned with distracted driving and safety.
A Matching-Based User Pairing and Resource Allocation Mechanism for V-MIMO Systems

In this paper, we investigate the joint user pairing and resource allocation (UP-RA) problem in uplink multi-user MIMO or virtual MIMO (V-MIMO) systems. Due to the practical transmission constraints, solving such a joint optimization problem is NP-hard. Therefore, we study this problem from a novel perspective, and propose a universal and efficient heuristic algorithm. First, we formulate the UP-RA problem as a special many-to-one matching problem with constraints. Different from most existing researches, we consider a universal scheduling problem, instead of a particular one. Then, we modify the classical deferred acceptance (DA) algorithm which is designed for the common many-to-one matching problem, and develop a lowcomplex heuristic algorithm called adapted deferred acceptance (ADA) algorithm for the UP-RA problem. Numerical results demonstrate that the ADA algorithm achieves robust and better performance compared with other representative algorithms in many scenarios.
Challenges Faced by Agile Testers: A Case Study

In the agile framework developers and testers are part of a hyper-productive, self-organizing team. Testing scope changes when user stories are added, updated, or deleted. Moreover, test cases need to be re-written or update when user stories change or removed from the sprint, which wastes time and makes agile testers frustrated. Furthermore, the mini-waterfall model adopted within a sprint could develop unwanted stress in agile testers. Despite these challenges little seem to be understood about the challenges faced by agile testers. We fill this gap by closely studying the challenges faced by agile testers in a multinational IT services organization. We first set up a forum for agile testers to discuss their challenges, malpractices, and best practices. We then conducted an online survey based on the findings from the forum and literature review. Through this, we identified 15 challenges and best practices to overcome those 15 challenges. We further conducted a set of interviews with the subject matter expertise to identify a suitable set of solutions such as conducting retrospective in a fun and engaging way, managing story lifecycle using collaborative tools, setting up ground rules for geographically distributed teams, and introducing innovative sprints.
It Takes Two, Baby: Style and Tangibles for Recommending and Interacting with Videos

Children, nowadays, are great consumers of media for them [6], and there is growing interest towards novel mechanisms that can consider their specific needs and improve both the recommendation process and output of videos for them. Children, in fact, have unique characteristics, which change with age. In particular, in the 8–12 age range, they like challenging interactions with systems, they enjoy exploring and “to feel the experts”. The majority of current recommendation solutions are unable to leverage on such specific features of children, and others, when interacting with them and recommending adequate videos. In this paper, we introduce ITTuB, an exploratory project, set at the intersection of recommender system and interaction design for children. Starting from previous research in the two areas, it plans to introduce tangibles for enhancing the interaction of children with videos, and to leverage stylistic features of videos in order to deliver recommendations that are optimized for children. This workshop paper presents the main ideas of ITTuB.
Сравнительный анализ методов автоматической классификации поэтических текстов на основе лексических признаков (Comparative Analysis of Methods of Automated Classification of Poetic Texts Based on Lexical Signs)

In this paper we analyze the principles of formation of the training samples for the algorithms of the definition of styles and genre types. The computational experiments with a corpus of texts of Lyceum lyrics of A. S. Pushkin at the choice of the most accurate algorithm of classification of poetic texts were conducted, including the usage of the best-known methods of assembling of the basic algorithms in the composition, such as weighted voting, boosting and stacking, and as a characteristic feature of the poems the single words, bigrams and trigrams were used. The considered algorithms showed their efficiency and can be used to automate the complex analysis of Russian poetic texts, significantly facilitating the work of the expert in determining of their styles and genres by providing the appropriate recommendations.
Insight Into the Formation of Lithium Alloys in All-Solid-State Thin Film Lithium Batteries

Solid-state thin-film batteries utilise electrode and electrolyte components which are nanometres or micrometres thick, enabling the production of novel devices with new form factors. Here, in situ X-ray diffraction is used to carry out the first study of a solid-state thin-film lithium-ion battery containing a solid-state LiPON electrolyte and Bi negative electrode. The structure-electrochemistry relationships in the Li-Bi system are revealed and details of cell construction, data collection, and data analysis is presented to guide for research.
LocRec: Rule-Based Successive Location Recommendation in LBSN

Successive location recommendation has recently emerged as an important service in Location-Based Social Networks (LBSNs). It aims at recommending the next location(s) to visit to a user given its current and previous locations. Although several recommenders have been proposed, only few works have considered the sequential correlations among locations in addition to other influential factors in recommendation. In this paper, a novel sequential rule mining-based approach called Location Recommender (LocRec) is proposed. Our proposal is designed to perform successive location recommendation for users of LBSN by considering sequential, social and temporal influence factors. The proposed approach first extracts the set of location sequences from mobility data and then generates recommendation rules from it. Based on the concept of recommendation influence factor, two rule-based recommenders are designed, namely temporal-based and social-based recommenders. An experimental evaluation was conducted on a real large-scale LBSN dataset to compare the performance of the proposed recommenders. Obtained results show that the tolerance to order-variations and the use of a window constraint enhance the performance of LocRec. They also depict that LocRec outperforms classical sequential-based models for successive location recommendation.
Fair Resource Allocation Over Time

We consider the over-time version of the Max-Min Fair Allocation problem. Given a time horizon $t=1,2,ldots, T$, with at each time t a set of demands and a set of available resources that may change over the time defining instance $I_t$, we seek a sequence of solutions $S_1, S_2, ldots , S_T$ that (1) are near-optimal at each time t , and (2) as stable as possible (inducing small modification costs). We focus on the impact of the knowledge of the future on the quality and the stability of the returned solutions by distinguishing three settings: the off-line setting where the whole set of instances through the time horizon is known in advance, the on-line setting where no future instance is known, and the k -lookahead setting where at time t , the instances at times $t+1, ldots, t+k$ are known. We first consider the case without restrictions where the set of resources and the set of agents are the same for all instances and where every resource can be allocated to any agent. For the off-line setting, we show that the over-time version of the problem is much harder than the static one, since it becomes $\mathcalNP $-hard even for families of instances for which the static problem is trivial. Then, we provide a $\fracρ ρ+1 $-approximation algorithm for the off-line setting using as subroutine a ρ-approximation algorithm for the static version. We also give a $\fracρ ρ+1 $-competitive algorithm for the online setting using also as subroutine a ρ-approximation algorithm for the static version. Furthermore, for the case with restrictions, we show that in the off-line setting it is possible to get a polynomial-time algorithm with the same approximation ratio as in the case without restrictions. For the online setting, we prove that it is not possible to find an online algorithm with bounded competitive ratio. For the 1-lookahead setting however, we give a $\fracρ 2 (2 ρ + 1) $-approximation algorithm using as subroutine a ρ-approximation algorithm for the static version.
Domain Adaptation for Disease Phrase Matching with Adversarial Networks

With the development of medical information management, numerous medical data are being classified, indexed, and searched in various systems. Disease phrase matching, i.e., deciding whether two given disease phrases interpret each other, is a basic but crucial preprocessing step for the above tasks. Being capable of relieving the scarceness of annotations, domain adaptation is generally considered useful in medical systems. However, efforts on applying it to phrase matching remain limited. This paper presents a domain-adaptive matching network for disease phrases. Our network achieves domain adaptation by adversarial training, i.e., preferring features indicating whether the two phrases match, rather than which domain they come from. Experiments suggest that our model has the best performance among the very few non-adaptive or adaptive methods that can benefit from out-of-domain annotations.
Multi-Modal Interfaces for Human-Robot Communication in Collaborative Assembly

Human–robot collaboration (HRC) in production, especially in assembly, has the potential to improve flexibility and competitiveness. However, there are still numerous challenges to address before HRC can be realised. Beyond the essential problem of safety, the efficient sharing of work and workspace between human and robot also requires new interfaces for communication. The SYMBIO-TIC H2020 project is developing a dynamic context-aware and bi-directional, multi-modal communication system that supports human operators in collaborative assembly.
Cronobacter sakazakii and Microbiological Parameters in Dairy Formulas Associated With a Food Alert in Chile

The objective of this study was to evaluate the presence of Cronobacter sakazakii and microbiological parameters in dairy products associated with a food alert. Ninety dairy product samples were analyzed, including seven commercial brands and two product types (liquid and powdered) from four countries. Aerobic plate count (APC) and Enterobacteriaceae count were performed according to Chilean standards. Cronobacter spp. and C. sakazakii were identified by polymerase chain reaction real time amplification of rpoB and cgcA genes and the genotype by multilocus sequence typing. Eighty-eight percent of dairy products showed APC higher than the detection limit. Fifty percent of liquid commercial brand samples contained APC: 2.6, 2.3, 1.1, and 2.9 CFU/mL in brands A, C, E, and G, respectively. Results for powdered commercial brands were 3.0, 3.6, and 5.7 CFU/g in brands B, D, and F, respectively. Maximum count (5.7 CFU/g) occurred in brand F dairy product manufactured in Chile. Enterobacteriaceae were found in 55% of the samples, 64% in liquid and 51% in powdered commercial brands. In 50% of brands B, D, and E, samples contained 2.9, 2.8, and 2.7 log CFU/g, respectively. Only liquid commercial brands from the United States had Enterobacteriaceae values between 0.1 and 4.5 CFU/mL. Seventeen suspicious strains were isolated and nine were identified as Enterobacter spp. Only eight suspicious strains from four powdered commercial brands (Chile and Singapore) were confirmed as C. sakazakii by rpoB and cgcA gene amplification and fusA sequencing. C. sakazakii prevalence in the analyzed samples was 8.8%. There were 11% of powdered milk brands that contained APC between 4.0 and 4.7 log CFU/g and 55% of the samples contained Enterobacteriaceae. C. sakazakii was found in dairy products manufactured in Chile and Singapore. On the basis of this information, the Chilean Ministry of Health (RSA) decreed a national and international food alert and recalled all the product batches that resulted positive in the present study from supermarkets and pharmacies.
Design Considerations of Ferroelectric Properties for Negative Capacitance MOSFETs

Impact of physical parameters of ferroelectric layer on the performance of Negative Capacitance (NC) MOSFETs is experimentally studied in this paper. Electrical behaviors of PZT-based and Si:HfO2-based NC-FETs are investigated and discussed. In a PZT-based p-type NC-FET, a sub-thermal swing down to 20mV/dec is achieved due to the remarkable voltage gain of NC, reaching a maximum value of 10V/V. Nevertheless, the performance improvements with Si:Hf5O2 NC booster are significantly lower than PZT due to the coexistence of different phases and also high leakage current which can enormously reduce the enhancement by NC.
Moving target approach for wind-aware flight path generation

A total loss of thrust poses a major hazard to passengers and aircrafts. In such situations, the pilot is forced to perform an emergency landing by fast and intuitive decisions. During the manoeuvre, the potential energy of the aircrafts altitude is converted into the kinetic energy to move a certain distance over ground. This may enable the aircraft to reach a suitable landing field at a proper altitude. In this paper, we introduce an emergency landing assistant which calculates the flight path from a start to a target position with uniform computational complexity. The main objective is to support the pilot and accelerate the decision-making process. Our path computations take a constant wind into account by moving the target runway contrary to the wind direction. The results have shown that even with the restriction of co-rotating circles a high percentage of valid flight paths can be found. Furthermore, for most of the considered start configurations more than one feasible flight path can be discovered.
Screening for Autism Spectrum Disorder: Profiles of Children Who Are Missed

Objective: To characterize children presenting with concerns for autism spectrum disorder (ASD) missed by parent-report screeners and to examine benefits of a combined screening approach with the Modified Checklist for Autism in Toddlers, Revised (M-CHAT-R) and the Ages and Stages Questionnaire, Third Edition (ASQ-3). Methods: Participants included were 154 children aged 16 to 42 months presenting for an evaluation at an autism center. Caregivers completed the M-CHAT-R, ASQ-3, and a demographic questionnaire. Children participated in an autism diagnostic evaluation consisting of the Mullen Scales of Early Learning (Mullen) and Autism Diagnostic Observation Schedule, Second Edition (ADOS-2). Results: A total of 124 children (81%) were diagnosed with ASD. The M-CHAT-R identified 85% (n = 105) of these children. Children with ASD missed by the M-CHAT-R had significantly higher scores on the Mullen and significantly lower scores on the ADOS-2. Of the ASQ-3 domains, the majority (n = 102, 82%) of children with ASD failed the communication domain; missed cases showed similar patterns of higher Mullen scores and lower ADOS-2 scores. When adopting a combined screening approach, using a failed screen from either the M-CHAT-R or ASQ-3 communication domain, 93% of children were identified. Parent-reported concerns on an open-ended questionnaire revealed ASD red flags for many missed cases. Conclusion: Children with ASD missed by screeners had higher scores on developmental testing and lower scores on the ADOS-2; however, children still performed below average on developmental tests. Our findings suggest that a combined screening approach was most effective for identifying children with ASD from a sample group referred for an ASD evaluation.
Real-Time Behavior Analysis and Identification for Android Application

The number of applications based on the Android platform is increasing rapidly now. However, as the supervision and review of Android applications are inadequate, a reasonable chance exists that users will download malware. This malware can lead to information leakage, monetary loss, and other damages. At present, a variety of applications exist for detecting malware, but most of these applications cannot show specific malicious behaviors. Moreover, the operation of this detection software is based on the database of viruses, and thus, it cannot identify unknown malware. To solve these problems, we implemented a system to detect the behaviors of Android applications and identify known or unknown malware. Our system can monitor specified applications utilizing loading a kernel module. After the detection process, the related documents are uploaded to the server, and the dynamic behaviors are reconstructed. As a result, a behavior diagram is generated. In addition, if the user needs to know whether the application is malware, the related Android package is sent to the server and analyzed. Then, the server calculates the results and the results are returned to the client.
Neural Morphological Tagging of Lemma Sequences for Machine Translation

Translation to morphologically rich languages is a difﬁcult task because of sparsity caused by morphological richness. In this work we perform a pilot study on predicting the morphologically rich POS tags of sequences of lemmas. Similar studies have been conducted in the context of phrase-based statistical machine translation. We implement a state-of-the-art tagger taking lemmas as input and show that we can successfully predict the morphologically rich POS tags, with accuracies of up to 91%
Knowledge-Driven Feed-Forward Neural Network for Audio Affective Content Analysis

Machine learning techniques have shown great promises across domains, however they fail to impress when there is sparsity of training data. Work in the area of affective content analysis can not take complete advantage of machine learning techniques when there is a lack of sufﬁcient training data. It is well known that recurrent neural networks (RNNs), particularly with long-short term memory (LSTM) units, perform better than feed-forward neural networks (FFNNs) on sequential data as they are architecturally designed to learn temporal relationships existing in the training data while FFNNs are not. But RNNs require sufﬁcient training data to learn these temporal relationships. In this paper, we show that one can take advantage of a-priori knowledge about the temporal correlations in the training data even in a FFNN architecture. We call this the knowledge-driven FFNN or k-FFNN architecture. We show using the MediaEval dataset that the k-FFNN model not only outperforms FFNN, but also performs better than RNN models (i.e, Simple RNN, RNN with LSTM units and bi-directional RNN with LSTM units (BLSTM)), especially when the amount of training data is sparse.