Miracle-dz/UniFAR-FTUs huggingface dataset:
DISCOVERY OF CUSTOMER SATISFACTION DIMENSION FROM TWEETS USING LATENT DIRICHLET ALLOCATION

Customer satisfaction of a service or product is reflected in the customer’s attitude towards the service or product once the customer perceived or used the service or product. Customer satisfaction has many dimensions, and previous studies have identified and used those many dimensions to measure the customer satisfaction of a certain service or product. In this study, we explore the use of text mining techniques of modeling topic as a way of discovering customer satisfaction dimensions, and we used tweets data from Twitter as our dataset. These are our contributions. We believed that this approach had not been used for discovering customer satisfaction dimensions. We used Latent Dirichlet Allocation for the topic modeling, and perplexity model and topic coherence are used for optimizing the number of topics. We would then choose representative words for each topic formed using Latent Dirichlet Allocation. The name of the topic is added by analyzing the logical relationships between the representative words and find the semantic meaning of the representative words in the context of the original tweets. These are done manually by the authors. Once the labels have been identified, we associate the labels to the original tweets. The identified topics in our study become customer satisfaction dimensions. The last task in our study is comparing the identified dimensions with dimensions in previous studies. In this task, we needed to broaden the definition of dimensions from previous studies. The result shows our identified dimensions are fitted with the dimensions from previous studies. The main differences are the naming of the dimensions and the granularity of the dimensions. Our identified dimensions have finer granularity than previous studies. Our study enables the decision maker to be informed with the current dimensions, which are mattered by the customer in using the organization’s services or products. Once the tweets are labeled to the identified topic names, we can sort the topics having the most documents. This result shows important issues which are discussed by the customers.
Machine Translation With Weakly Paired Documents

Neural machine translation, which achieves near human-level performance in some languages, strongly relies on the large amounts of parallel sentences, which hinders its applicability to low-resource language pairs. Recent works explore the possibility of unsupervised machine translation with monolingual data only, leading to much lower accuracy compared with the supervised one. Observing that weakly paired bilingual documents are much easier to collect than bilingual sentences, e.g., from Wikipedia, news websites or books, in this paper, we investigate training translation models with weakly paired bilingual documents. Our approach contains two components. 1) We provide a simple approach to mine implicitly bilingual sentence pairs from document pairs which can then be used as supervised training signals. 2) We leverage the topic consistency of two weakly paired documents and learn the sentence translation model by constraining the word distribution-level alignments. We evaluate our method on weakly paired documents from Wikipedia on six tasks, the widely used WMT16 German\leftrightarrowEnglish, WMT13 Spanish\leftrightarrowEnglish and WMT16 Romanian\leftrightarrowEnglish translation tasks. We obtain 24.1/30.3, 28.1/27.6 and 30.1/27.6 BLEU points separately, outperforming previous results by more than 5 BLEU points in each direction and reducing the gap between unsupervised translation and supervised translation up to 50%.
Wavelength and Bandwidth Allocation for Mobile Fronthaul in TWDM-PON

Time- and wavelength- division multiplexed passive optical network (TWDM-PON) has attracted considerable attention for the next generation optical access systems. Among potential applications of TWDM-PON, a major application is the support of mobile fronthaul streams between radio units (RUs) and distributed units (DUs) in the centralized radio access network (C-RAN) architecture, which consists of central units (CUs), DUs, and RUs. The upstream fronthaul traffic that an optical line terminal (OLT) receives is expected to become highly bursty due to the variable data rate generated by employing new functional split options and the synchronization of data transmission between neighboring RUs caused by time-division duplex (TDD). However, there has been no wavelength and bandwidth allocation scheme for TWDM-PON that is designed to efficiently accommodate fronthaul streams satisfying the strict delay requirement. Therefore, in this paper we propose a novel wavelength and bandwidth allocation algorithm that can minimize the number of active wavelength channels considering the high burstiness and delay requirement of fronthaul data transmission. Through computer simulations it was confirmed that the number of active wavelength channels can be reduced by 50% with the proposed algorithm, and thus more RUs can be efficiently accommodated using TWDM-PON.
Tracking and Behavior Augmented Activity Recognition for Multiple Inhabitants

We develop CACE (Constraints And Correlations mining Engine), a framework that significantly improves the recognition accuracy of complex daily activities in multi-inhabitant smarthomes. CACE views the implicit relationships between the activities of multiple people as an asset, and exploits such constraints and correlations in a hierarchical fashion, taking advantage of both person-specific sensor data (generated by wearable devices) and person-independent ambient sensor data (generated by ambient sensors). To effectively utilize such couplings, CACE first uses a multi-target particle filtering approach over ambient sensors captured movement data, to identify the number of distinct users and infer individual-specific movement trajectories. We then utilize a Hierarchical Dynamic Bayesian Network (HDBN)-based model for activity recognition. This model utilizes the inter-and-intra individual correlations and constraints, at both micro-activity and macro-activity levels, to recognize individual activities accurately. These constraints are learnt automatically using data-mining techniques, and help to dramatically reduce the computational complexity of HDBN-based inferencing. Empirical studies using a real-world testbed of five multi-inhabitant smarthomes shows that CACE is able to achieve an activity recognition accuracy of <inline-formula><tex-math notation="LaTeX">$\approx 95\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>≈</mml:mo><mml:mn>95</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="alam-ieq1-2936382.gif"/></alternatives></inline-formula>, with a 16-fold reduction in computational overhead compared to traditional hybrid classification approaches.
Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training

The objective of non-parallel text style transfer, or controllable text generation, is to alter specific attributes (e.g. sentiment, mood, tense, politeness, etc) of a given text while preserving its remaining attributes and content. Generative adversarial network (GAN) is a popular model to ensure the transferred sentences are realistic and have the desired target styles. However, training GAN often suffers from mode collapse problem, which causes that the transferred text is little related to the original text. In this paper, we propose a new GAN model with a word-level conditional architecture and a two-phase training procedure. By using a style-related condition architecture before generating a word, our model is able to maintain style-unrelated words while changing the others. By separating the training procedure into reconstruction and transfer phases, our model is able to learn a proper text generation process, which further improves the content preservation. We test our model on polarity sentiment transfer and multiple-attribute transfer tasks. The empirical results show that our model achieves comparable evaluation scores in both transfer accuracy and fluency but significantly outperforms other state-of-the-art models in content compatibility on three real-world datasets.
Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text

Condition is essential in scientific statement. Without the conditions (e.g., equipment, environment) that were precisely specified, facts (e.g., observations) in the statements may no longer be valid. Existing ScienceIE methods, which aim at extracting factual tuples from scientific text, do not consider the conditions. In this work, we propose a new sequence labeling framework (as well as a new tag schema) to jointly extract the fact and condition tuples from statement sentences. The framework has (1) a multi-output module to generate one or multiple tuples and (2) a multi-input module to feed in multiple types of signals as sequences. It improves F1 score relatively by 4.2% on BioNLP2013 and by 6.2% on a new bio-text dataset for tuple extraction.
Solar Powered Robotic Vehicle for Optimal Battery Charging Using PIC Microcontroller

This paper focuses on the design and construction of an optimization charging system for Li–Po batteries by means of tracked solar panels. Thus, the implementation of a complete energy management system applied to a robotic exploration vehicle is put forward. The proposed system was tested on the VANTER robotic platform — an autonomous unmanned exploration vehicle specialized in recognition. The interest of this robotic system lies in the design concept, based on a smart host microcontroller. On this basis, our proposal makes a twofold significant contribution. On the one hand, it presents the construction of a solar tracking mechanism aimed at increasing the rover’s power regardless of its mobility. On the other hand, it proposes an alternative design of power system performance based on a pack of two batteries. The aim is completing the process of charging a battery independently while the other battery provides all the energy consumed by the robotic vehicle.
Physico-chemical, Antioxidant Properties and Sensory Attributes of Golden Melon (Cucumis melon L)-Watermelon (Citrullus lanatus) Juice Blends

This study investigates the physico-chemical, antioxidant properties and sensory attributes of golden melon-watermelon fruits juice blends. The juice extracted from golden melon and watermelon fruits were mixed together in different ratios 100% watermelon (GMW 1), 100% golden melon (GMW 2), 50% golden melon and 50% watermelon (GMW 3), 70% golden melon and 30% watermelon (GMW 4), 30% golden melon and 70% watermelon (GMW 5) in order to establish possible synergetic effect. The proximate composition of the samples ranged from 92.07 – 93.91%; 0.49 – 0.81%; 1.83 – 4.91%; 1.80 – 3.62% for moisture content, total ash, crude protein and carbohydrate respectively. The pH of the fruit juice blends ranged from 5.40 to 5.48. There is no significant difference (p<0.05) among the fruits juice samples. The mineral composition of the fruit juice for sodium (Na), potassium (K), iron (Fe), and calcium (Ca) ranged from 17.01 to 41.76 mg/100 g, 9.26 to 38.51 mg/100 g, 0.38 to 1.88 mg/100g, and 3.49 to 8.96 mg/100g respectively. The result shows that the total phenolic content ranged from 6.32-10.20 mg GEA/ 100 g; total flavonoid ranged from 0.08-0.13 mg QAE/ 100 g; DPPH ranged from 55-80%; ABTS ranged from 0.022-0.025 mmol. TEAC/ 100 g and FRAP ranged from 1.20-4.23 mg AAE/g. The results of sensory attributes of the fruit juice revealed that, fruit juice GMW 5 was the most preferable in aroma, taste and overall acceptability by the panelist. Hence, golden melon-watermelon fruit juice blends may serve as a source of strong antioxidant property that may be used to improve health status.
Towards Robust Retrieval for Imperfectly Scanned Point Cloud Objects

With the development of 3D model analysis and particularly on classification challenge, the algorithms are getting better and better. Since no large dataset of scanned models is available, evaluating the algorithms in real-life scenarios is not straightforward. For now, these studies rely on ModelNet, a dataset of CAD models. Moreover, no studies considered the robustness to common recording data corruptions like occlusion or noise. In this paper, we present a preliminary study to assess the retrieval performances of point cloud-based algorithms for occluded or noisy objects. The experiment shows very promising results, even in the case of deep learning networks which has been pre-trained with CAD models. Indeed, more than 90% of the objects are retrieved when even only 40% of the query object is visible.
Digital Continuity Guarantee Method of Urban Construction Archives Based on Risk Management

: With the broad application of information technology in urban infrastructure, urban construction has entered the stage of a smart city, forming many electronic records in urban construction. These electronic records play a vital role in the maintenance of urban infrastructure. However, electronic records often change in the process of urban construction. How to preserve the electronic records of urban construction became a significant challenge. In response to this problem, this paper proposes the use of risk-based management techniques to ensure the digital continuity, authenticity, integrity, and availability of electronic records.
Physical Layer Security in Wireless Sensor Networks

Due to the broad application of wireless sensor networks (WSNs), the security of these systems have become a necessity. This thesis focuses on the physical layer security of WSNs using chaotic Direct Sequence Spread Spectrum (chaotic-DSSS). The thesis begins with investigating the overall security threats to WSNs, whereby the possible attacks on different Open Systems Interconnection (OSI) layers have been reviewed and analyzed, showing their vulnerability to various security attacks due to hardware limitation of WSNs. One of the primary attacks against WSNs is jamming, the effect of which on the physical layer of WSNs is investigated in this thesis. The WSNs in this study use chaotic-DSSS modulation, as Chaotic-DSSS provides secrecy and jamming resistance. The jamming resistance performance of chaotic-DSSS under the perfect chip synchronization is evaluated. Chaotic-DSSS is extremely dependant on chip synchronization. The synchronization performance against different jammers is also investigated based on the probabilities of detection and false alarm. It is shown that chip synchronization in chaotic-DSSS is vulnerable to jamming attacks. The effect of the probabilities of detection and false alarm on bit error rate is investigated by proposing the probability of successful synchronization. The results demonstrate the significant effects of jammer on the probability of bit error of chaotic-DSSS in the case of imperfect chip synchronization. Based on this vulnerability, this thesis also proposes a new security attack called Syncim. In this attack, the adversary interferes with the synchronization pilot signals of the legitimate transmitter. Then, it sends its fake synchronization pilot signals and makes the receiver (victim) sync with the adversary instead of the legitimated transmitter. The sensitivity of the chaotic-DSSS to the chip synchronization has been an incentive for researchers to investigate different methods to remove the chip synchronization and maintain the secrecy feature of the chaotic-DSSS. Furthermore, the chaotic-DSSS system requires a random number generation for each chip, which makes it complicated to implement in WSNs. This thesis proposes a fix chaotic-DSSS system, and investigates its secrecy performance. Also, a fake user method is proposed to enhance the secrecy of the fix chaotic-DSSS.
Memory Grounded Conversational Reasoning

We demonstrate a conversational system which engages the user through a multi-modal, multi-turn dialog over the user’s memories. The system can perform QA over memories by responding to user queries to recall specific attributes and associated media (e.g. photos) of past episodic memories. The system can also make proactive suggestions to surface related events or facts from past memories to make conversations more engaging and natural. To implement such a system, we collect a new corpus of memory grounded conversations, which comprises human-to-human role-playing dialogs given synthetic memory graphs with simulated attributes. Our proof-of-concept system operates on these synthetic memory graphs, however it can be trained and applied to real-world user memory data (e.g. photo albums, etc.) We present the architecture of the proposed conversational system, and example queries that the system supports.
Robust Nonlinear Multiple Unmanned Surface Vessels Control Against Probabilistic Faults and Time-Varying Delay

This paper is concerned with robust nonlinear control for multiple unmanned surface vessel (MUSV) systems. Firstly, a mixed nonlinear model was built, in which the probabilistic actuator and fault time-varying communication delay was considered. On the basis of the new model, a robust nonlinear control approach by utilizing Lyapunov-Krosovskii functional is proposed, which can robustly stabilize the MUSV system with a given level of disturbance attenuation ensure the MUSV can track well. Furthermore, additional conditions were established for the designed controller to guaranteeing mesh stability. Eventually, the superiority and effectiveness of the proposed control strategies are verified by numerical simulation.
Uncertainty through Sampling: The Correspondence of Monte Carlo Dropout and Spiking in Artificial Neural Networks

Any organism that senses its environment only has an incomplete and noisy perspective on the world, which creates a necessity for nervous systems to represent uncertainty. While the principles of encoding uncertainty in biological neural ensembles are still under investigation, deep learning became a popular and effective machine learning method. In these models, sampling through dropout has been proposed as a mechanism to encode uncertainty. Moreover, dropout has previously been linked to variability in spiking networks under specific assumptions. We compare the relationship between dropout and spiking neuron models by means of the variation ratio over their output. We demonstrate that in cases of incomplete world knowledge (epistemic uncertainty) as well as for noisy observations (aleatoric uncertainty) both neuron models show similar uncertainty representations. These findings provide evidence that sampling could play a fundamental role in representing uncertainties in neural systems.
When the God Meets a Tribal Girl: Narasiṃha’s Second Marriage in the Light of the Vāsantikāpariṇayam

When the God Meets a Tribal Girl: Narasiṃha’s Second Marriage in the Light of the Vāsantikāpariṇayam A widely diffused pattern of a recognized god who takes a second wife, usually local, has essentially articulated the acculturation of tribes or other spatially and socially separated groups. This motif has been discussed regarding South Indian literary traditions, where two brides are opposites in terms of origin, status and appearance, and a double marriage metaphor that aims at reconciliation of two distant spheres should be often contextualized within bhakti ideology. The motif of unconditional devotion of the additional wife to her husband is also closely connected to Vijayanagara politics: a local girl as a spouse may reflect the extension of both royal and spiritual power symbolized by the god. The present paper explores the strategy and purpose of the adaptive re-use of a vernacular legend from the area of Ahobilam about the love between Narasiṃha and a Cencū huntress, as extolled by the author of a Sanskrit drama entitled Vāsantikāpariṇayam.
The Network-Integrated Storage System

We present NICE, a key-value storage system design that leverages new software-defined network capabilities to build cluster-based network-efficient storage system. NICE presents novel techniques to co-design network routing and multicast with storage replication, consistency, and load balancing to achieve higher efficiency, performance, and scalability. We implement the NICEKV prototype. NICEKV follows the NICE approach in designing four essential network-centric storage mechanisms: request routing, replication, consistency, and load balancing. Our evaluation shows that the proposed approach brings significant performance gains compared with the current systems design: up to <inline-formula><tex-math notation="LaTeX">$7\times $</tex-math><alternatives><mml:math><mml:mrow><mml:mn>7</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="alkiswany-ieq1-2938158.gif"/></alternatives></inline-formula> put/get performance improvement, up to <inline-formula><tex-math notation="LaTeX">$2\times $</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="alkiswany-ieq2-2938158.gif"/></alternatives></inline-formula> reduction in network load, <inline-formula><tex-math notation="LaTeX">$3\times $</tex-math><alternatives><mml:math><mml:mrow><mml:mn>3</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="alkiswany-ieq3-2938158.gif"/></alternatives></inline-formula> to <inline-formula><tex-math notation="LaTeX">$9\times $</tex-math><alternatives><mml:math><mml:mrow><mml:mn>9</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="alkiswany-ieq4-2938158.gif"/></alternatives></inline-formula> load reduction on the storage nodes, and the elimination of scalability bottlenecks present in current designs.
DEEPPOREID: An Effective Pore Representation Descriptor in Direct Pore Matching

This paper proposes an effective pore representation descriptor based on Convolutional Neural Networks (CNNs). We make full use of the diversity and large quantities of sweat pores in fingerprints to learn a deep feature, denoted as DeepPoreID. The DeepPoreID is then used to describe the local feature for each pore and finally integrated into the classical direct pore matching method. Experiments carried on the challenge public high-resolution fingerprint database with small image size of 320 × 240 shows the effectiveness of the proposed DeepPoreID. The results also have shown that the proposed method outperforms other existing state-of-the-art methods in the aspect of recognition accuracy. About ~35% rise in accuracy can be obtained when compared with the best result achieved by existing methods.
SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations

Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed. This paper introduces SyntagNet, a novel resource consisting of manually disambiguated lexical-semantic combinations. By capturing sense distinctions evoked by syntagmatic relations, SyntagNet enables knowledge-based WSD systems to establish a new state of the art which challenges the hitherto unrivaled performances attained by supervised approaches. To the best of our knowledge, SyntagNet is the first large-scale manually-curated resource of this kind made available to the community (at http://syntagnet.org).