Miracle-dz/UniFAR-FTUs huggingface dataset:
SC-RANK: Improving Convolutional Image Captioning with Self-Critical Learning and Ranking Metric-based Reward

Image captioning usually employs a Recurrent Neural Network (RNN) to decode the image features from a Convolutional Neural Network (CNN) into a sentence. This RNN model is trained under Maximum Likelihood Estimation (MLE). However, inherent issues like the complex memorising mechanism of the RNNs and the exposure bias introduced by MLE exist in this approach. Recently, the convolutional captioning model shows advantages with a simpler architecture and a parallel training capability. Never-theless, the MLE training brings the exposure bias which still prevents the model from achieving better performance. In this paper, we prove that the self-critical algorithm can optimise the CNN-based model to alleviate this problem. A ranking metric-based reward, denoted as SC-RANK, is proposed with the sentence embeddings from a pre-trained language model to generate more diversiﬁed captions. Applying SC-RANK can avoid the tedious tuning of the specially-designed language model and the knowledge transferred from a pre-trained language model proves to be helpful for image captioning tasks. State-of-the-art results have been obtained in the MSCOCO dataset by proposed SC-RANK.
Implementation of Blockchain Technology in Accounting Sphere

Blockchain technology is inherent in the importance of accounting property - information in the database is reliable, truthful, regardless of the confidence of the counterparty. Transaction occurs only if it has been approved by all participants. With the help of network accounting can be translated into a secure, transparent for all parties and easy to use format. The introduction of technology in accounting has the following advantages: online transactions are high-speed and user-friendly; accounts can be updated using smartphone apps; optical data recognition systems allow you to automate the entire process, starting with the introduction of primary documents.
Personality Traits as Predictor of M-Payment Systems: A SEM-Neural Networks Approach

Mobile phones have led to a great revolution of modern society, helpful for many businesses to reorient their sales methods towards effective commercial formats. The m-payment, for instance, as an emergent technology to these novel commercial setups, is now undertaking the adoption process. Individual users are known to vary in their tendency to accept new technologies. Not surprisingly, some conceptual models describe how and why individuals use m-payments. Until recently, however, the role of personality in overall, and the big five model of personality, in particular, had remained mostly unexplored. This article aims to ascertain the impact of personality traits on m-payment adoption. Data were collected from 323 m-payment customers and analyzed using a two-step research methodology. SEM was applied to test the hypothesis, and significant antecedents of m-payment were identified. Next significant personality factors were input to a neural network model for ranking. The results showed that conscientious and agreeableness is the two main predictors of m-payment adoption.
A Novel System for Extractive Clinical Note Summarization using EHR Data

While much data within a patient’s electronic health record (EHR) is coded, crucial information concerning the patient’s care and management remain buried in unstructured clinical notes, making it difficult and time-consuming for physicians to review during their usual clinical workflow. In this paper, we present our clinical note processing pipeline, which extends beyond basic medical natural language processing (NLP) with concept recognition and relation detection to also include components specific to EHR data, such as structured data associated with the encounter, sentence-level clinical aspects, and structures of the clinical notes. We report on the use of this pipeline in a disease-specific extractive text summarization task on clinical notes, focusing primarily on progress notes by physicians and nurse practitioners. We show how the addition of EHR-specific components to the pipeline resulted in an improvement in our overall system performance and discuss the potential impact of EHR-specific components on other higher-level clinical NLP tasks.
Structural dynamics of big gantry crane subjected to different trolley move laws

The present work concerns the oscillations and therefore the displacements present on a portal crane in the case in which the trolley is moved with different laws and in particular with different maximum accelerations. The work also investigates the effects of an abrupt stop of the trolley movement and also how the load oscillations, following the stop of the trolley movement, generates vibrations in the crane structure which are correlated to the crane natural frequencies. The work was developed on a real geometry of portal cranes using the finite element method. A new procedure was developed and implemented to simulate variable forces on the crane i.e. the forces induced by the trolley movement. The results show that in determinate conditions, the displacement and therefore the relative oscillations are very significant and therefore cannot be neglected.
Factors affecting the integration of local traditional products into quality assurance schemes

This paper investigates the factors that affect the decisions of the Prespes Beans? (Fassolia 'Prespon') producers to certify their product as a product of protected geographical indication (PGI). The study was based on primary data collected by means of a questionnaire completed by both PGI certified and non-certified producers. The logit model was used for the indication of factors that affect the producers? decision to certify or not to certify their product as PGI. Descriptive statistics were also used for the study of leaders? social features. The research results showed that the producers? decision of adopting the protected geographical indication (PGI) certification was significantly linked to membership producer organizations (cooperatives or other groups), the years of agricultural experience, the size of the cultivated land as well as the motives for joining quality assurance schemes.
Development of a RISC-V-Conform Fused Multiply-Add Floating-Point Unit

Despite the fact that the open-source community around the RISC-V instruction set architecture is growing rapidly, there is still no high-speed open-source hardware implementation of the IEEE 754-2008 floating-point standard available. We designed a Fused Multiply-Add Floating-Point Unit compatible with the RISC-V ISA in SystemVerilog, which enables us to conduct detailed optimizations where necessary. The design has been verified with the industry standard simulation-based Universal Verification Methodology using the Specman e Hardware Verification Language. The most challenging part of the verification is the reference model, for which we integrated the Floating-Point Unit of an existing Intel processor using the Function Level Interface provided by Specman e. With the use of Intel's Floating-Point Unit we have a ``known good" and fast reference model. The Back-End flow was done with Global Foundries' 22 nm Fully-Depleted Silicon-On-Insulator (GF22FDX) process using Cadence tools. We reached 1.8 GHz over PVT corners with a 0.8 V forward body bias, but there is still a large potential for further RTL optimization. A power analysis was conducted with stimuli generated by the verification environment and resulted in 212 mW.
BSDP: Big Sensor Data Preprocessing in Multi-Source Fusion Positioning System Using Compressive Sensing

A multi-source fusion positioning system (MFPS) adopts a data fusion technique for hybrid positioning. This technique fully utilizes several types of positioning sources in contrast to the conventional positioning methods, which use a single positioning source. Sensor data collection and processing are essential components in MFPSs. A framework for a practical center-server-based MFPS is proposed in this paper. A big sensor data preprocessing (BSDP) scheme, which is composed of data extraction (DE-BSDP), data gathering (DG-BSDP), and data transmission (DT-BSDP), is further proposed under this framework. DE-BSDP attempts to remove useless positioning data from fusion sources. The big sensor data are further compressed in DG-BSDP, in which a compressive sensing technique is adopted to realize data compression before data transmission. After the data gathering phase, the compressed data are transmitted to the fusion center for reconstruction in DT-BSDP. The proposed BSDP method can reduce the data collection amount significantly and improves the data transmission efficiency with a slight reduction on positioning accuracy. Experiments and simulations verify the effectiveness of the proposed BSDP scheme.
A Comparative Study of Misapplied Crypto in Android and iOS Applications

Many applications for Android and iOS process sensitive data and, therefore, rely on cryptographic APIs natively provided by the operating system. For this to be effective, essential rules need to be obeyed, as otherwise the attainable level of security would be weakened or entirely defeated. In this paper, we inspect the differences between Android and iOS concerning the proper usage of platform-specific APIs for cryptography. For both platforms, we present concrete strategies to detect critical mistakes and introduce a new framework for Android that excels in pinpointing the origin of problematic security attributes. Applied on real-world apps with cryptography, we find that out of 775 investigated apps that vendors distribute for both Android and iOS, 604 apps for iOS (78%) and 538 apps for Android (69%) suffer from at least one security misconception.
On the Optimal Recovery Threshold of Coded Matrix Multiplication

We provide novel coded computation strategies for distributed matrix–matrix products that outperform the recent “Polynomial code” constructions in recovery threshold, i.e., the required number of successful workers. When a fixed <inline-formula> <tex-math notation="LaTeX">$1/m$ </tex-math></inline-formula> fraction of each matrix can be stored at each worker node, Polynomial codes require <inline-formula> <tex-math notation="LaTeX">$m^{2}$ </tex-math></inline-formula> successful workers, while our MatDot codes only require <inline-formula> <tex-math notation="LaTeX">$2m-1$ </tex-math></inline-formula> successful workers. However, MatDot codes have higher computation cost per worker and higher communication cost from each worker to the fusion node. We also provide a systematic construction of MatDot codes. Furthermore, we propose “PolyDot” coding that interpolates between Polynomial codes and MatDot codes to trade off computation/communication costs and recovery thresholds. Finally, we demonstrate a novel coding technique for multiplying <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> matrices (<inline-formula> <tex-math notation="LaTeX">$n \geq 3$ </tex-math></inline-formula>) using ideas from MatDot and PolyDot codes.
Software Defined Wireless Mesh Network Flat Distribution Control Plane

Wireless Mesh Networks (WMNs), have a potential offering relatively stable Internet broadband access. The rapid development and growth of WMNs attract ISPs to support users’ coverage anywhere anytime. To achieve this goal network architecture must be addressed carefully. Software Defined Networking (SDN) proposes new network architecture for wired and wireless networks. Software Defined Wireless Networking (SDWN) has a great potential to increase efficiency, ease the complexity of control and management, and accelerate technology innovation rate of wireless networking. An SDN controller is the core component of an SDN network. It needs to have updated reports of the network status change, as in network topology and quality of service (QoS) in order to effectively configure and manage the network it controls. In this paper, we propose Flat Distributed Software Defined Wireless Mesh Network architecture where the controller aggregates entire topology discovery and monitors QoS properties of extended WMN nodes using Link Layer Discovery Protocol (LLDP) protocol, which is not possible in multi-hop ordinary architectures. The proposed architecture has been implemented on top of POX controller and Advanced Message Queuing Protocol (AMQP) protocol. The experiments were conducted in a Mininet-wifi emulator, the results present the architecture control plane consistency and two application cases: topology discovery and QoS monitoring. The current results push us to study QoS-routing for video streaming over WMN.
Explicitly Modeling Word Translations in Neural Machine Translation

In this article, we show that word translations can be explicitly incorporated into NMT effectively to avoid wrong translations. Specifically, we propose three cross-lingual encoders to explicitly incorporate word translations into NMT: (1) Factored encoder, which encodes a word and its translation in a vertical way; (2) Gated encoder, which uses a gated mechanism to selectively control the amount of word translations moving forward; and (3) Mixed encoder, which stitchingly learns a word and its translation annotations over sequences where words and their translations are alternatively mixed. Besides, we first use a simple word dictionary approach and then a word sense disambiguation (WSD) approach to effectively model the word context for better word translation. Experimentation on Chinese-to-English translation demonstrates that all proposed encoders are able to improve the translation accuracy for both traditional RNN-based NMT and recent self-attention-based NMT (hereafter referred to as Transformer). Specifically, Mixed encoder yields the most significant improvement of 2.0 in BLEU on the RNN-based NMT, while Gated encoder improves 1.2 in BLEU on Transformer. This indicates the usefulness of an WSD approach in modeling word context for better word translation. This also indicates the effectiveness of our proposed cross-lingual encoders in explicitly modeling word translations to avoid wrong translations in NMT. Finally, we discuss in depth how word translations benefit different NMT frameworks from several perspectives.
Communications in Multi-Antenna Interference Networks

In this work we examine communications systems with multiple interfering pairs of transmitters and receivers, where each node may have multiple antennas. We give an overview of the information theoretic results for such interference channels and for the remainder of the work focus on the suboptimal linear strategies, where interference is treated as noise. For single-antenna and multi-antenna systems we review known analytical results and numerically compare the performance of different known distributed algorithms. We present the multi-antenna extension of the interference pricing algorithm and propose some asymptotic performance approximations.
How robust are measures of online control to offline mechanisms

The importance of online visual feedback for the accurate completion of reaching movements is widely accepted. In contrast, the quantification of online feedback utilization can still be challenged. For example, many contemporary measures of online control are calculated across multiple trials, which brings about potential confounds via the contribution of offline trial-to-trial changes in performance. The current study sought to contrast the robustness of common measures of online control and a novel frequency-based measure (de Grosbois & Tremblay, 2015) to offline-induced changes in reaching trajectories. The main experimental conditions included reaches performed in no-vision conditions with: a) optical prisms that shifted the perceived location of the target and environment and b) terminal feedback [TF] via a 1-s window of vision after each movement. Also, control full-vision conditions were performed with and without TF. The measures of online control included two between-trial measures (i.e., variable error [VE] and normalized correlations of position at 75 % of movement time relative to the end-position [Z2]) and two within trial measures (i.e., the time-after-peak-velocity [TAPV] and a novel frequency analysis [pPower]). The results indicated that VE and TAPV were sensitive to the prism manipulation (i.e., not robust to offline control mechanism), whereas Z2 and pPower were relatively immune to the offline changes caused by the prisms. Supplementary analyses comparing the two full-vision conditions (i.e., with and without TF) indicated that the pPower measure was the most robust to offline influences. Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), Ontario Research Fund (ORF)
Classification of Semantic Paraphasias: Optimization of a Word Embedding Model

In clinical assessment of people with aphasia, impairment in the ability to recall and produce words for objects (anomia) is assessed using a confrontation naming task, where a target stimulus is viewed and a corresponding label is spoken by the participant. Vector space word embedding models have had inital results in assessing semantic similarity of target-production pairs in order to automate scoring of this task; however, the resulting models are also highly dependent upon training parameters. To select an optimal family of models, we fit a beta regression model to the distribution of performance metrics on a set of 2,880 grid search models and evaluate the resultant first- and second-order effects to explore how parameterization affects model performance. Comparing to SimLex-999, we show that clinical data can be used in an evaluation task with comparable optimal parameter settings as standard NLP evaluation datasets.
Dyslexia Research and the Partial Report Task: A First Step toward Acknowledging Iconic and Visual Short-term Memory

ABSTRACT The potential role of iconic memory in dyslexia-specific partial report deficits has never been investigated although it may help distinguish between different visuo-attentional theories of dyslexia. The present study is a first step towards such an investigation within an iconic memory framework. 20 French-speaking university students with dyslexia and 20 controls were administered the partial report task. Five-item strings were displayed for a short duration and participants had to report one item in a cued position (post-cueing condition). A pre-cueing condition was used to control for potential visual and attentional differences in letter identification performance between the dyslexic and control groups. The group difference in accuracy was larger in the post-cueing condition than in the pre-cueing condition. This suggests that crowding and symbol-sound mapping are not causing the dyslexia-specific deficit in partial report tasks, and that iconic memory is a plausible interpretative framework for visuo-attentional deficits in dyslexia.
Effects of amplitude ramps on phonemic restoration of compressed speech with normal-hearing and hearing-impaired listeners

Continuity illusion refers to the phenomenon where an interrupted signal is perceived as continuous, once the silent interval is filled with a louder sound. A similar mechanism is believed to help with phonemic restoration of missing speech segments. When speech segments are not audible due to masking of interfering background sounds, listeners may fill in the gaps and have enhanced speech intelligibility, even when those segments are omitted. For example, as a special case of phonemic restoration, it was shown that recognition of gated sentences was better when the silent intervals were filled with loud noise bursts. The present study is a preliminary attempt in exploring hearing aid processing effects on complex listening tasks that are likely to occur in real life, such as restoration of obliterated speech. Specifically, we explored if phonemic restoration might be degraded due to hearing aid compression, which might produce ramps on the speech envelope during the recovery from compression following the loud noise bursts lling the gaps. In Experiment 1, phonemic restoration was measured with normal-hearing (NH) listeners where ramps of var- ying durations were added on the speech envelope after the noise intervals, to simulate recovery from compression. Phonemic restoration was significantly reduced as the duration of the ramps increased. Experiment 2 shows preliminary results with hearing-impaired (HI) listeners, where phonemic restoration was measured for a number of configurations. The results showed a large variability in phonemic restoration by HI listeners, and audibility and gating period were observed to be important factors affecting the results. The combined results imply that hearing aid compression might have detrimental effects on phonemic restoration; however, more data is needed to determine how applicable these results would be to HI listeners.
Combining n-grams and deep convolutional features for language variety classification

Abstract This paper presents a novel neural architecture capable of outperforming state-of-the-art systems on the task of language variety classification. The architecture is a hybrid that combines character-based convolutional neural network (CNN) features with weighted bag-of-n-grams (BON) features and is therefore capable of leveraging both character-level and document/corpus-level information. We tested the system on the Discriminating between Similar Languages (DSL) language variety benchmark data set from the VarDial 2017 DSL shared task, which contains data from six different language groups, as well as on two smaller data sets (the Arabic Dialect Identification (ADI) Corpus and the German Dialect Identification (GDI) Corpus, from the VarDial 2016 ADI and VarDial 2018 GDI shared tasks, respectively). We managed to outperform the winning system in the DSL shared task by a margin of about 0.4 percentage points and the winning system in the ADI shared task by a margin of about 0.2 percentage points in terms of weighted F1 score without conducting any language group-specific parameter tweaking. An ablation study suggests that weighted BON features contribute more to the overall performance of the system than the CNN-based features, which partially explains the uncompetitiveness of deep learning approaches in the past VarDial DSL shared tasks. Finally, we have implemented our system in a workflow, available in the ClowdFlows platform, in order to make it easily available also to the non-programming members of the research community.
THE PREPARATION OF TEACHERS IN THE FIELD OF MULTIMEDIA EDUCATION

The current large development of information and communication technologies is also reflected in the education process, its modernization and the introduction of new technologies into education. Multimedia as a modern technology has entered the traditional teaching system and it can be a useful tool in the hands of future teachers with the necessary theoretical and practical knowledge. The teacher can have a positive impact on the learning process and on the students themselves by using multimedia in education. Therefore multimedia education is now becoming an educational trend as an integral part of the preparation of future pedagogues. E-learning as the most sophisticated way of education provides many opportunities how to use multimedia in the teaching process. Multimedia education courses can be accessed through communication networks and they enable students to get the latest educational content from multiple sources at any time and from anywhere. The contribution provides information about multimedia and its use in the education of future teachers. It also describes the issue of creating the content of an e-learning course that aims to teach future teachers how to create a multimedia aid suitable for inclusion into the education. Through our research and its analysis, it describes the impact of our e-learning course on students as future teachers and the results determine recommendations for the teaching practice.