Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simple Baseline to Semi-Supervised Domain Adaptation for Machine Translation

State-of-the-art neural machine translation (NMT) systems are data-hungry and perform poorly on new domains with no supervised data. As data collection is expensive and infeasible in many cases, domain adaptation methods are needed. In this work, we propose a simple but effect approach to the semi-supervised domain adaptation scenario of NMT, where the aim is to improve the performance of a translation model on the target domain consisting of only non-parallel data with the help of supervised source domain data. This approach iteratively trains a Transformer-based NMT model via three training objectives: language modeling, back-translation, and supervised translation. We evaluate this method on two adaptation settings: adaptation between specific domains and adaptation from a general domain to specific domains, and on two language pairs: German to English and Romanian to English. With substantial performance improvement achieved---up to +19.31 BLEU over the strongest baseline, and +47.69 BLEU improvement over the unadapted model---we present this method as a simple but tough-to-beat baseline in the field of semi-supervised domain adaptation for NMT.
Graph Neural Network Encoding for Community Detection in Attribute Networks

In this article, we first propose a graph neural network encoding method for the multiobjective evolutionary algorithm (MOEA) to handle the community detection problem in complex attribute networks. In the graph neural network encoding method, each edge in an attribute network is associated with a continuous variable. Through nonlinear transformation, a continuous valued vector (i.e., a concatenation of the continuous variables associated with the edges) is transferred to a discrete valued community grouping solution. Further, two objective functions for the single-attribute and multiattribute network are proposed to evaluate the attribute homogeneity of the nodes in communities, respectively. Based on the new encoding method and the two objectives, a MOEA based upon NSGA-II, called continuous encoding MOEA, is developed for the transformed community detection problem with continuous decision variables. Experimental results on single-attribute and multiattribute networks with different types show that the developed algorithm performs significantly better than some well-known evolutionary- and nonevolutionary-based algorithms. The fitness landscape analysis verifies that the transformed community detection problems have smoother landscapes than those of the original problems, which justifies the effectiveness of the proposed graph neural network encoding method.
Time Series Data Cleaning with Regular and Irregular Time Intervals

Errors are prevalent in time series data, especially in the industrial field. Data with errors could not be stored in the database, which results in the loss of data assets. Handling the dirty data in time series is non-trivial, when given irregular time intervals. At present, to deal with these time series containing errors, besides keeping original erroneous data, discarding erroneous data and manually checking erroneous data, we can also use the cleaning algorithm widely used in the database to automatically clean the time series data. This survey provides a classification of time series data cleaning techniques and comprehensively reviews the state-of-the-art methods of each type. In particular, we have a special focus on the irregular time intervals. Besides we summarize data cleaning tools, systems and evaluation criteria from research and industry. Finally, we highlight possible directions time series data cleaning.
Multimodal Systems: Taxonomy, Methods, and Challenges

Naturally, humans use multiple modalities to convey information. The modalities are processed both sequentially and in parallel for communication in the human brain, this changes when humans interact with computers. Empowering computers with the capability to process input multimodally is a major domain of investigation in Human-Computer Interaction (HCI). The advancement in technology (powerful mobile devices, advanced sensors, new ways of output, etc.) has opened up new gateways for researchers to design systems that allow multimodal interaction. It is a matter of time when the multimodal inputs will overtake the traditional ways of interactions. The paper provides an introduction to the domain of multimodal systems, explains a brief history, describes advantages of multimodal systems over unimodal systems, and discusses various modalities. The input modeling, fusion, and data collection were discussed. Finally, the challenges in the multimodal systems research were listed. The analysis of the literature showed that multimodal interface systems improve the task completion rate and reduce the errors compared to unimodal systems. The commonly used inputs for multimodal interaction are speech and gestures. In the case of multimodal inputs, late integration of input modalities is preferred by researchers because it allows easy update of modalities and corresponding vocabularies.
Prevention of Payment Card Frauds using Biometrics

Digitization era has paved the path for digital economy. The advent of plastic cards such as credit and debit cards facilitate many services and products anytime and anywhere but they suffer with fraudulent attacks. This paper proposes a novel approach for the prevention of payment card frauds using biometrics. The approach works with the existing payment card security system and takes the advantage of biometric recognition technology that serves with a unique ID and bears anindividual’s biometric data like Aadhaar. The integration of biometric verification system with the payment card security system not only strengthens the security of card users but also it ensures the physical presence of card holder at the point of sale. The system like Aadhaar may be successfully linked with the payment card processing system for supplementing identity verification of users and making the environment free from card frauds. The performance of the proposed payment card security system using biometrics shows that the probability of breaching user’s identity is significantly low and found to be in the range of − to − for face, fingerprint and iris recognition
Coronavirus research before 2020 is more relevant than ever, especially when interpreted for COVID-19

The speed with which biomedical specialists were able to identify and characterize COVID-19 was partly due to prior research with other coronaviruses. Early epidemiological comparisons with Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), also made it easier to predict COVID-19’s likely spread and lethality. This article assesses whether academic interest in prior coronavirus research has translated into interest in the primary source material, using Mendeley reader counts for early academic impact evidence. The results confirm that SARS and MERS research in 2008–2017 experienced anomalously high increases in Mendeley readers in April–May 2020. Nevertheless, studies learning COVID-19 lessons from SARS and MERS or using them as a benchmark for COVID-19 have generated much more academic interest than primary studies of SARS or MERS. Thus, research that interprets prior relevant research for new diseases when they are discovered seems to be particularly important to help researchers to understand its implications in the new context.
Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-Based Traffic Congestion Control Systems

Recent work has shown that the introduction of autonomous vehicles (AVs) in traffic could help reduce traffic jams. Deep reinforcement learning methods demonstrate good performance in complex control problems, including autonomous vehicle control, and have been used in state-of-the-art AV controllers. However, deep neural networks (DNNs) render automated driving vulnerable to machine learning-based attacks. In this work, we explore the backdooring/trojanning of DRL-based AV controllers. We develop a trigger design methodology that is based on well-established principles of traffic physics. The malicious actions include vehicle deceleration and acceleration to cause stop-and-go traffic waves to emerge (congestion attacks) or AV acceleration resulting in the AV crashing into the vehicle in front (insurance attack). We test our attack on single-lane and two-lane circuits. Our experimental results show that the backdoored model does not compromise normal operation performance, with the maximum decrease in cumulative rewards being 1%. Still, it can be maliciously activated to cause a crash or congestion when the corresponding triggers appear.
Density of states approach to the hexagonal Hubbard model at finite density

We apply the linear logarithmic relaxation (LLR) method, which generalizes the Wang-Landau algorithm to quantum systems with continuous degrees of freedom, to the fermionic Hubbard model with repulsive interactions on the honeycomb lattice. We compute the generalized density of states of the average Hubbard field and devise two reconstruction schemes to extract physical observables from this result. By computing the particle density as a function of chemical potential we assess the utility of LLR in dealing with the sign problem of this model, which arises away from half filling. We show that the relative advantage over brute-force reweighting grows as the interaction strength is increased and discuss possible future improvements.
Thoracic Disease Identification and Localization using Distance Learning and Region Verification

The identification and localization of diseases in medical images using deep learning models have recently attracted significant interest. Existing methods only consider training the networks with each image independently and most leverage an activation map for disease localization. In this paper, we propose an alternative approach that learns discriminative features among triplets of images and cyclically trains on region features to verify whether attentive regions contain information indicative of a disease. Concretely, we adapt a distance learning framework for multi-label disease classification to differentiate subtle disease features. Additionally, we feed back the features of the predicted class-specific regions to a separate classifier during training to better verify the localized diseases. Our model can achieve state-of-the-art classification performance on the challenging ChestX-ray14 dataset, and our ablation studies indicate that both distance learning and region verification contribute to overall classification performance. Moreover, the distance learning and region verification modules can capture essential information for better localization than baseline models without these modules.
Taxonomic Study of Viridans Streptococci: Description of Streptococcus gordonii sp. nov. and Emended Descriptions of Streptococcus sanguis (White and Niven 1946), Streptococcus oralis (Bridge and Sneath 1982), and Streptococcus mitis (Andrewes and Horder 1906)

We examined a collection of 151 strains of the viridans type of streptococci, which were isolated mainly from human oral cavities and included several reference strains, in an attempt to revise and improve the taxonomy of this group of bacteria. Our examinations included determinations of a high number of biochemical and physiological characteristics and serological reactivity. The resulting data revealed several hitherto unrecognized characters of taxonomic significance, and several of the species can now be more accurately defined. A diagnostic key to the taxa is presented. Strains previously identified as Streptococcus sanguis could be divided into two clearly distinct species, Streptococcus sanguis sensu stricto (type strain, ATCC 10556) and a new species, Streptococcus gordonii (type strain, ATCC 10558). Streptococcus mitis was divided into two biovars, consisting of strains possessing group O antigens and strains possessing group K antigen. The group of strains assigned to Streptococcus anginosus was biochemically and serologically heterogeneous, but the data did not allow natural subdivisions. Based on the results of this study, emended descriptions of the species Streptococcus oralis, S. mitis, and S. sanguis are provided. The classification resulting from this study is in complete agreement with previously published genetic data.
Distributed Approximation on Power Graphs

We investigate graph problems in the following setting: we are given a graph G and we are required to solve a problem on G2. While we focus mostly on exploring this theme in the distributed CONGEST model, we also show new results and surprising connections to the centralized model of computation. In the CONGEST model, it is natural to expect that problems on G2 would be quite difficult to solve efficiently on G, due to congestion. However, we show that the picture is both more complicated and more interesting. Specifically, we encounter two phenomena acting in opposing directions: (i) slowdown due to congestion and (ii) speedup due to structural properties of G2. We demonstrate these two phenomena via two fundamental graph problems, namely, Minimum Vertex Cover (MVC) and Minimum Dominating Set (MDS). Among our many contributions, the highlights are the following. (1) In the CONGEST model, we show an O(n/∈)-round (1 + ∈)-approximation algorithm for MVC on G2, whereas no o(n2)-round algorithm is known for any better-than-2 approximation for MVC on G. (2) We show a centralized polynomial time 5/3-approximation algorithm for MVC on G2, whereas a better-than-2 approximation is UGC-hard for G. (3) In contrast, for MDS, in the CONGEST model, we show an [EQUATION] lower bound for a constant approximation factor for MDS on G2, whereas an Ω(n2) lower bound for MDS on G is known only for exact computation.
Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation

We propose a semantics-driven unsupervised learning approach for monocular depth and ego-motion estimation from videos in this paper. Recent unsupervised learning methods employ photometric errors between synthetic view and actual image as a supervision signal for training. In our method, we exploit semantic segmentation information to mitigate the effects of dynamic objects and occlusions in the scene, and to improve depth prediction performance by considering the correlation between depth and semantics. To avoid costly labeling process, we use noisy semantic segmentation results obtained by a pre-trained semantic segmentation network. In addition, we minimize the position error between the corresponding points of adjacent frames to utilize 3D spatial information. Experimental results on the KITTI dataset show that our method achieves good performance in both depth and ego-motion estimation tasks.
Listeria species in seafood: isolation and characterization of Listeria spp. from seafood in San Luis, Argentina

One hundred seafood samples (fish, squid and mussel) were collected from the Argentine Atlantic coast and screened for Listeria spp. The isolates were characterized by biochemical tests, serotyping, phage typing and macrorestriction enzymes analysis of DNA (pulsed-field gel electrophoresis). The overall frecuency (n=100) ofListeria spp. was 12%. Of the 12 isolated strains, three strains isolated from different squid samples were identified as L. monocytogenes and nine strains from fish, mussels and squid as L. innocua. All three L. monocytogenes strains belonged to serovar 4b; eight strains of L. innocua were serovar 6a and one strain of L. innocua was serovar 6b. All three L. monocytogenes strains, but only one of the nine L. innocua strains, were phage-typeable. One restriction profile was detected with Apa I and Asc I for L. monocytogenes strains and three with the same enzymes for L. innocua strains. The combination of these patterns allowed definition of four distinct groups within the 12 strains. The results of this study showed that phenotypic methods remain appropriate but that pulsed-field gel electrophoresis is useful for epidemiological purposes.
Transversus Abdominis Plane Block for Laparoscopic Hysterectomy Pain: A Meta-Analysis

Objective: Review the analgesic effect of the transversus abdominis plane (TAP) block and its impact on postoperative pain scores and opioid usage for patients undergoing laparoscopic and robotic hysterectomies. Methods: Systematic review with meta-analysis of randomized controlled trials that compared the effect of TAP block to either placebo or no block on narcotic use (in morphine equivalent units [MEq]) and pain (per visual analog scale) within 24] h after a laparoscopic or robotic hysterectomy for benign or malignant indications. Searches were conducted in PubMed and Embase through May 31, 2019. Results: Nine randomized controlled trials met eligibility criteria; 7 evaluated laparoscopic hysterectomy and 2 robotic hysterectomy. A total of 688 subjects were included (559 laparoscopic hysterectomy, 129 robotic hysterectomy). Opioid consumption was similar in the first 24] h postoperative with or without TAP block (−0.8 MEq; 95% CI, −2.9, 1.3; 8 TAP arms; N] = 395). Pain scores (visual analog scale) were also similar with or without TAP block (−0.01 U; 95% CI, −0.34, 0.32; 10 TAP arms; N] = 636). Neither meta-analysis showed statistical heterogeneity across studies. Conclusions: The evidence does not support a benefit of TAP block to reduce pain or opioid use for patients receiving laparoscopic or robotic hysterectomies.
Arbuscular mycorrhizal (AM) status and seedling growth response to indigenous AM colonisation of Euryodendron excelsum in China: implications for restoring an endemic and critically endangered tree

It is increasingly evident that the mycorrhizal colonisation of endangered species is of major importance for their restoration. In the present study, the symbiosis of arbuscular mycorrhizal fungi (AMF) and endangered species Euryodendron excelsum was investigated in 10 patches of a remnant population in south China. The presence of arbuscules and vesicles indicates that E. excelsum is a typical arbuscular mycorrhizal plant. Five genera were identified in the rhizosphere of E. excelsum, and the most common and frequent genus was Glomus. Root total colonisation intensity is negatively correlated with the available soil phosphorus and potassium content in the soil. In addition, we find no significant relationship between spore density and soil characteristics, or between spore density and total colonisation intensity. Furthermore, a greenhouse experiment under two soil types (humus: native soil = 3 : 1 ST1; humus: native soil = 1 : 3 ST2) was conducted to evaluate the effects of AMF inoculation on seedling growth. The levels of plant mycorrhizal response of E. excelsum seedlings under the ST1 and ST2 soil types were 136 and 413%, respectively. Although a significant growth enhancement was found in the ST1 soil type, seedling growth and survival rate were improved after AMF colonisation under both soil types. The results suggest that AMF colonisation may have practical implications in establishing effective conservation and restoration strategies for this critically endangered plant.
Micropropagation of an elite F1 watermelon (Citrullus lanatus) hybrid from the shoot tip of field grown plants

The aim of this work was to develop a protocol for rapid micropropagation of an elite F1 hybrid watermelon cultivar using shoot tip of field-grown plants. Maximum frequency (73%) of shoot tip showed growth response in MS medium supplemented with 5 mg l-1 benzyl adenine (BA) and 0.1 mg l-1 indole-3 acetic acid (IAA). Upon transfer to cytokinin-enriched medium, the cultures produced multiple shoots and 2.0 mg l-1 BA was optimum in this respect. Addition of gibberellic acid (GA3) in the multiplication medium resulted in better growth of shoots. Rooting rate was 100% when shoots were obtained from second subculture were cultured in medium with 1.0 mg l-1 indole-3 butyric acid (IBA). The shoots produced more roots with increasing number of subcultures. About 72% of the regenerated plantlets acclimatized successfully and survived in the soil condition.
Influence of maize particle size on the kinetics of starch digestion in the small intestine of growing pigs

The kinetics of starch digestion in the small intestine, nutrient utilisation and the performance of growing pigs that were fed diets containing maize of different particle sizes were evaluated. For the performance evaluation, 42 barrows and females (initial weight: 30.5 ± 1.78 kg) were used in a randomised block design with three treatments [maize with different geometric mean diameters (GMD) of 550, 700 and 850 μm] and seven replications. To evaluate the kinetics of starch digestion, one animal from each experimental trial was sacrificed. The small intestine was removed and separated into five equal parts. The starch digestibility in each segment was determined. To determine the total digestibility and nitrogen balance, 24 barrows (initial weight: 41.3 ± 4.72 kg) were housed in metabolism cages in a randomised block design with three treatments and eight repetitions. The animals that were fed diets containing maize of 550-μm GMD exhibited better (P = 0.008) feed conversion. However, the digestion of starch was gradual and variable among the degrees of grinding that were tested. There were higher (P = 0.008; 0.005 and 0.009) digestible energy, dry matter digestibility and nitrogen uptake in diets containing the maize ground to 550 and 700 μm in GMD. Thus, the smallest GMD of maize (550 μm) that was used in diets for growing pigs improves feed conversion. GMD of 550, 700 and 850 μm of maize were associated with rapid, intermediate and slow starch digestion, respectively.
Chroococcidiopsis (Cyanophyceae), a phycobiont in the lichen family Lichinaceae1

Chroococcidiopsis occurred as a phycobiont in members of the lichen family Lichinaceae. The phycobionts isolated from Anema nummularium (strain 26322a PH), Peccania sp. (strain 26370a PH) and Psorotichia sp. (strain 26391c PH) proved to be three different strains of Chroococcidiopsis, a genus of blue-green algae (cyanobacteria). The mode of division and reproduction was studied in cultures of the algae. The non-motile baeocytes (‘endospores’), which are characteristic of the genus, were produced by multiple fission either of secondary or tertiary mother cells. Within particular strains the mode of development varies. In strain 26322a PH, baeocytes were formed only by secondary mother cells; in strain 26370a PH, both types occurred. In strain 26391c PH, baeocytes were produced by two forms of secondary mother cells: globose mother cells with an irregular arrangement of baeocytes and by clusters of four mother cells with more or less regular arrangement of baeocytes. It is not possible to identify capsoid b.
Flux compactifications on toric varieties

Smooth, compact toric varieties (SCTV) can admit a reduced structure group, and thus be suitable as internal manifolds in flux compactifications. We review the construction of SU(3) structures on three‐dimensional SCTV proposed in [1]. We summarise the techniques used to construct the reduced structure group and read off the torsion classes. Some examples illustrate the proposed method.