Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain adaptation for statistical machine translation

In this thesis we develop and evaluate a general framework for domain-adaptation of statistical machine translation (SMT) systems. The framework relies on the availability of in-domain training data and a scoring scheme to differentiate the other-domain training instances. Adapted models include various models used in the translation process, but more focus is given to the less researched phrase model adaptation. The language model is utilized in many applications, e.g. speech recognition and character recognition, and domain adaptation has been extensively researched for this model. Domain-adaptation is the task of adapting an existing general-domain system to perform better on a target domain evaluation set. Weighting the training data has been proposed in the past to perform domain adaptation. Prominent previous work used language model perplexities to perform the scoring. We present a general framework to perform the weighting. Moreover, we develop few novel scoring models that rely on translation models scores to perform the scoring. We hypothesize that for translation model adaptation, translation model scores are more relevant than language model scores as the former captures bilingual dependencies which are fundamental for the translation task. The main part of the thesis includes the development of few scoring schemes for adaptation. Novelties include the usage of IBM Model 1 perplexities, and more prominently, developing a method to generate translation model scores representing relatedness to the target domain. The methods are evaluated consistently on competitive Arabic-to-English and German-to-English translation tasks and significant improvements in translation quality are reported. A limitation of the framework presented is the reliance on in-domain training data. We tackle the scenario where no explicit bilingual in-domain training data exists to perform adaptation. We rely on monolingual source test data to induce the domain and show that the novel usage of automatic translations of the source test data can improve over a state-of-the-art SMT system. We expand the notion of domain to include information about dialects. In our setup we tackle the translation of dialectal Egyptian Arabic to English. A dialect classifier is developed within this work achieving state-of-the-art classification accuracy. The classifier is then used in several techniques to perform adaptation of a general SMT system and improvements are reported. Finally, we present work on Arabic segmentation for machine translation. Arabic is a morphologically rich language, where each word is composed of several morphemes that correspond to several words in English. Different segmentation schemes and models are implemented within this work. We show that the performance of the segmentation schemes varies according to the domain and a careful design of a scheme is required to perform best on a given domain. A combination strategy is then presented and best practices of performing the combination are discussed.
AIS Electronic Library (AISeL)

Prior research on privacy disclosure primarily focuses on conscious factors leading to intentional disclosure. In this study, we identify two unconscious factors, i.e., the habit of self-disclosure and the impulse of self-disclosure, which lead to users’ privacy disclosure behavior in social networking sites (SNS). We contribute to the existent literature by investigating the effects of these two factors on privacy disclosure behavior in SNS and examining a contingent factor for the effects of these two unconscious factors. Our results reveal that both habit and impulse have significant effects on privacy disclosure in SNS. The effects of habit and impulse are moderated by users’ privacy self-efficacy. Particularly, privacy self-efficacy weakens the effects of impulse on self-disclosure but strengthens the effect of habit on self-disclosure.
Collaborative Design-in-use

The fast-growing proliferation of multi-device systems has been reshaping the contexts in which collaborative activity takes place. The evolving materialisation of multi-device environments (MDEs) is likely to have an impact on foundational CSCW research, in ways that go beyond studying cross-device interaction. Designers, developers and researchers are reporting emerging challenges in understanding, supporting and designing for complex collaborative activity in MDEs. We argue that the theoretical perspective of Instrumental Genesis can help unveil the complex, dynamic relationships between design, people, tools, tasks and activities in technology-rich MDEs. In this paper, we use extracts from our research on collaborative work in an MDE to illustrate how ideas from the theory of Instrumental Genesis can help reveal important aspects of change and stability. We show how collaborative design-in-use contributes to the joint evolution of MDEs and the working practices unfolding within them.
Fusion of Hyperspectral and Panchromatic Images using Structure Tensor

In this paper, a new hyperspectral image fusion method with structure tensor is proposed. The proposed method utilizes PCA transformation to obtain the spatial details of HS image. Then, an image enhancement approach is applied to the PAN image to sharpen spatial information. Since structure tensor represents structure and spatial information, structure tensor is introduced to extract spatial details of the enhanced PAN image. Unlike traditional methods which extract details only from PAN image, the proposed method considers spatial details of the HS and PAN images simultaneously, and a weighted fusion method is presented to integrate spatial details of the two images to obtain complete spatial details. Finally, an injection gains matrix is constructed to reduce spectral and spatial distortion, and the fused image is generated by injecting the complete spatial information. Experimental results demonstrate that the proposed method obtains the excellent performance in both objective and subjective evaluations.
Cognitive impairment correlates with serum carbonyl compound profiles in subclinical carotid atherosclerosis

Current evidence indicates that carotid atherosclerosis is an independent risk factor for cognitive impairment. Serum metabolomic analysis holds significant promise for uncovering the relationship between carotid atherosclerosis and cognitive impairment. In this study, we aimed to evaluate the profiling of serum carbonyl compounds in subclinical carotid atherosclerosis (SCA) patients and to explore the relationship between serum carbonyl compounds and cognitive performance. We enrolled 51 SCA patients and 45 healthy control individuals using carotid ultrasound assessment. All the participants were subjected to a neuropsychological assessment and their fasting serum samples were collected for untargeted stable isotope-labeling strategy combined with liquid chromatography–double precursor ion scan–mass spectrometry analysis. Compared with the control, the SCA group showed lower scores in global cognition, immediate memory, verbal fluency, executive function, and visual attention. For the isotope-labeling strategy combined with liquid chromatography–double precursor ion scan–mass spectrometry analysis, 149 potential carbonyl candidates were discovered in the pooled serum. In the SCA serum, 41 carbonyl compounds showed significantly increased levels and 14 carbonyl compounds showed significantly decreased levels. In addition, six carbonyl compounds involved in the oxidation of polyunsaturated fatty acids and vitamin E were correlated with cognitive performance. A negative correlation was observed between cognitive performance and the levels of octanal, nonanal, &agr;-tocopherolquinone, and heptanal, respectively. A positive correlation was observed between cognitive performance and the levels of acetophenone and 1-(3-aminopropyl)-4-aminobutanal, respectively. In summary, the SCA individuals have poor cognitive performance, which may be reflected by aberrant serum carbonyl compound profiles.
Resveratrol, Metabolic Syndrome, and Gut Microbiota

Resveratrol is a polyphenol which has been shown to have beneficial effects on metabolic syndrome-related alterations in experimental animals, including glucose and lipid homeostasis improvement and a reduction in fat mass, blood pressure, low-grade inflammation, and oxidative stress. Clinical trials have been carried out to address its potential; however, results are still inconclusive. Even though resveratrol is partly metabolized by gut microbiota, the relevance of this “forgotten organ” had not been widely considered. However, in the past few years, data has emerged suggesting that the therapeutic potential of this compound may be due to its interaction with gut microbiota, reporting changes in bacterial composition associated with beneficial metabolic outcomes. Even though data is still scarce and for the most part observational, it is promising nevertheless, suggesting that resveratrol supplementation could be a useful tool for the treatment of metabolic syndrome and its associated conditions.
Review of National Cybersecurity Policies

The damages caused by cyber-attacks are becoming larger, broader and more serious as well as to include monetary losses and losses of lifeline. Some cyber-attacks are arguably suspected to be parts of national campaigns. Under such circumstances, efforts by the private sector are not enough to counteract against cyber-attacks, and the public sector must endeavour to enhance national cybersecurity capacities so that the nation can retain safe and secure life of nationals. We think it very important to ensure that the national cybersecurity strategies are sufficient, appropriate and up-to-dated, so that the national capacities progress towards the right direction at the adequate speed. Thus, we devised the method to review national cybersecurity strategies and used it to review the UK cybersecurity strategy. The result proved that the carefully formulated national strategies still need review and the method should work.
Pricing vs. Product-Oriented Web Technologies and Product Returns in E-marketplace: The Mediating Role of Page Views

Product returns are typically seen as a necessary headache and a cost drain for e-marketplace sellers. Although previous studies have explored the influences of web technologies on product returns, little is known about the mechanisms under which different categories of web technologies work. More importantly, extant studies on page views suggest a conflict relationship between product page views and product returns and attribute it to the moderating influences of information technology-enabled systems, but few studies have considered distinguishing the home page views and product page views. In this study, we developed a research model that incorporates both the differential influences of pricing vs. product oriented web technologies on home page views vs. product page views and the mediating role of page views in the relationship between web technologies and product returns by drawing on elaboration likelihood model and expectation confirmation theory. We plan to validate it by using a unique longitudinal dataset of observations of 4,000 sellers over ten months in the biggest e-marketplace in China. The research and practice implications are discussed.
The Frequency of Resurgery after Percutaneous Lumbar Surgery Using Dekompressor in a Ten-Year Period

To prevent open surgical procedures, minimally invasive techniques, like Dekompressor (PLDD), have been developed. The absence of reherniation is an important factor correlating with clinical success after lumbar surgery. In this retrospective, observational study, the frequency of additional open surgery after PLDD in a long time retrospective was examined. The correlation between clinical symptoms and outcome was assessed, and the time between PLDD and open surgery was analyzed. Consecutive patients after PLDD between 2005 and 2007 were included. MacNab's outcome criteria were used to evaluate patient satisfaction. The need for additional open surgery of the lumbar spine, the period between Dekompressor and resurgery, and the treated levels were analyzed. In total, 73 patients were included in this study. The patients were seen one month after PLDD. The majority of patients (76.7%) had additional radicular pain. The most common level treated was L4-5 (58.9%). The follow-up time was longer than 5 years in 30.1% of the patients and longer than 10 years in 6.82%. The short-term success rate was 67.1%. Additional surgery was performed in 26.0% of patients, with 78.9% of the reoperations undertaken during the first year after PLDD. These patients had a statistically significant worse outcome (P = 0.025). Radicular pain was present in all patients with an early subsequent surgery, but only in 50% of patients with late surgery (P = 0.035). Significantly more patients with poor pain relief had radicular pain (P = 0.04). The short-term success rate was worsened by a resurgery rate of 26.0%. Subsequent surgery, a short time after PLDD, suggests that PLDD is not a replacement for open discectomy. Because patients with radicular pain had a worse outcome and more frequent resurgeries, whether radicular pain is an ideal indication for PLDD should be discussed.
Lactate dehydrogenase A: A key player in carcinogenesis and potential target in cancer therapy

Elevated glycolysis remains a universal and primary character of cancer metabolism, which deeply depends on dysregulated metabolic enzymes. Lactate dehydrogenase A (LDHA) facilitates glycolytic process by converting pyruvate to lactate. Numerous researches demonstrate LDHA has an aberrantly high expression in multiple cancers, which is associated with malignant progression. In this review, we summarized LDHA function in cancer research. First, we gave an introduction of structure, location, and basic function of LDHA. Following, we discussed the transcription and activation mode of LDHA. Further, we focused on the function of LDHA in cancer bio‐characteristics. Later, we discussed the clinical practice of LDHA in cancer prevention and treatment. What we discussed gives a precise insight into LDHA especially in cancer research, which will contribute to exploring cancer pathogenesis and its handling measures.
Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks

Caching close to users in a radio access network (RAN) has been identified as a promising method to reduce a backhaul traffic load and minimize latency in 5G and beyond. In this paper, we investigate a novel community detection inspired by a proactive caching scheme for device-to-device (D2D) enabled networks. The proposed scheme builds on the idea that content generated/accessed by influential users is more probable to become popular and thus can be exploited for pro-caching. We use a Clustering Coefficient based Genetic Algorithm (CC-GA) for community detection to discover a group of cellular users present in close vicinity. We then use an Eigenvector Centrality measure to identify the influential users with respect to the community structure, and the content associated to it is then used for pro-active caching using D2D communications. The numerical results show that, compared to reactive caching, where historically popular content is cached, depending on cache size, load and number of requests, up to 30% more users can be satisfied using a proposed scheme while achieving significant reduction in backhaul traffic load.
Landsat 9 Thermal Infrared Sensor 2 Architecture and Design

The Thermal Infrared Sensor 2 (TIRS-2) will fly aboard the Landsat 9 spacecraft and leverages the Thermal Infrared Sensor (TIRS) design currently flying on Landsat 8. TIRS-2 will provide similar science data as TIRS, but is not a build-to-print rebuild due to changes in requirements and improvements in absolute accuracy. The heritage TIRS design has been modified to reduce the influence of stray light and to add redundancy for higher reliability over a longer mission life. The TIRS-2 development context differs from the TIRS scenario, adding to the changes. The TIRS-2 team has also learned some lessons along the way.
A Fast Parametric Model of Estimating Atmospheric Parameters for Landsat 8 Thermal Infrared Sensor

The traditional methods used in the atmospheric correction depend on the empirical relationships or atmospheric radiative transfer model. However, both of them have some deficiencies. For example, as the empirical method depends highly on the training data, it will be applicable under certain conditions. On the other hand, the method that based on the atmospheric radiative transfer model has to run the code each time, which is not an appropriate choice for operational correction of the atmospheric effects. In this paper, a fast parametric model of estimating atmospheric parameters for Landsat 8 thermal infrared sensor is proposed. The results show that the RMSE (Root Mean Squared Error) of the total transmission is 0.003, the RMSE of for both the atmospheric upward and downward radiances are 0.0004. Therefore, the proposed model could be used without the help of any atmospheric radiative transfer model. That is, this model would have a better application market.
PoTiA: A Popularity and Timeout Analysis Based SDN Controller Protection Approach

Software-defined networking (SDN) is a novel and promising network architecture, which decouples the controlling function from the forwarding plane. SDN provides the flexibility to program the network through centralized control. However, security issues of SDN should arouse our attention. In this paper, we mainly discuss a specific vulnerability of the centralized control mechanism in SDN, which is likely to suffer denial-of-service (DoS) flooding attack. We propose a popularity and timeout analysis-based controller protection approach to protect the controller from the flooding attack. We develop a controller protection application on the SDN controller in which a popularity table is maintained. When the arriving rate of the packets to the controller exceeds the pre-defined threshold, the selected proactive flow table entries will be installed on the data plane switches to ensure that the requests to the most popular destination addresses can be served with higher priority. Furthermore, we mitigate the unpopular requests to a low priority queue, which can send Packet_In requests to the controller with rate limiting. The timeout analysis module in the application can identify the malicious host by analyzing the lifetime of the flows according to the flow-removed messages. Blocking entries will be added to the blacklist table on the switch. Our controller protection approach can effectively alleviate the impact of the SDN controller-oriented flooding attack. The detection rate is 99.90%, and the false alarm rate is 0.41%.
Consistent Regression of Biophysical Parameters with Kernel Methods

This paper introduces a novel statistical regression framework that allows the incorporation of consistency constraints. A linear and nonlinear (kernel-based) formulation are introduced, and both imply closed-form analytical solutions. The models exploit all the information from a set of drivers while being maximally independent of a set of auxiliary, protected variables. We successfully illustrate the performance in the estimation of chlorophyll content.
5G New Radio Numerologies and their Impact on the End-To-End Latency

In this paper, we use a New Radio (NR) simulator, based on ns-3, to assess the impact of 5G NR numerologies on the end-to-end (E2E) latencies in a realistic and complex scenario, including TCP and UDP flows. As expected, we found that TCP goodput increases with the numerology, since a larger numerology allows reducing the round-trip-time. However, although counter-intuitive, simulation results exhibit that the E2E latency of uplink (UL) UDP flows may not be reduced with the numerology. In fact, it depends on two key factors and their relationship: the processing delays (fixed or numerologydependent) and the inter-packet arrival time, which depends on the UDP flow rate and the packet size. We demonstrate how, in some cases, the latency is worsened by an increasing signaling exchange that grows with the numerology. In particular, this is due to a handshake mechanism in UL (scheduling request and UL grant) that is performed each time a data packet encounters empty RLC buffers. For some combination of flow rate, packet size, and processing delays that are not numerology dependent, increasing the numerology may not reduce the E2E delay. Therefore, we conclude that the selection of the numerology in an NR system should be carefully made by taking into account the traffic patterns and the processing delays.
DeepDPM: Dynamic Population Mapping via Deep Neural Network

Dynamic high resolution data on human population distribution is of great importance for a wide spectrum of activities and real-life applications, but is too difficult and expensive to obtain directly. Therefore, generating fine-scaled population distributions from coarse population data is of great significance. However, there are three major challenges: 1) the complexity in spatial relations between high and low resolution population; 2) the dependence of population distributions on other external information; 3) the difficulty in retrieving temporal distribution patterns. In this paper, we first propose the idea to generate dynamic population distributions in full-time series, then we design dynamic population mapping via deep neural network(DeepDPM), a model that describes both spatial and temporal patterns using coarse data and point of interest information. In DeepDPM, we utilize super-resolution convolutional neural network(SRCNN) based model to directly map coarse data into higher resolution data, and a timeembedded long short-term memory model to effectively capture the periodicity nature to smooth the finer-scaled results from the previous static SRCNN model. We perform extensive experiments on a real-life mobile dataset collected from Shanghai. Our results demonstrate that DeepDPM outperforms previous state-of-the-art methods and a suite of frequent data-mining approaches. Moreover, DeepDPM breaks through the limitation from previous works in time dimension so that dynamic predictions in all-day time slots can be obtained.
The rs878081 polymorphism of AIRE gene increases the risk of rheumatoid arthritis in a Chinese Han population: a case-control study

The autoimmune regulator (AIRE), a transcriptional regulator expressed in medullary thymic epithelial cells, plays an important role in thymocyte education and negative selection. Several citations studying the association between the rs878081 exon polymorphism of the AIRE gene and the risk of rheumatoid arthritis (RA) in different populations have yielded conflicting findings. Thus, this case-control study involving 300 RA cases and 300 controls was aimed to identify whether such association existed in a Chinese Han population from East China. The rs878081 polymorphism of the AIRE gene was genotyped. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using the chi-squared test, genetic model analysis, and stratification analysis. Genetic model analysis showed significant correlations between the TT genotype and the risk of RA (OR: 1.89, 95%CI: 1.03-3.47 in TT vs CC; OR: 1.84, 95%CI: 1.02-3.31 in TT vs CC+TC). Stratification analyses of sex, age, smoking, and alcoholism suggested that the rs878081 polymorphism of the AIRE gene increased RA risk among non-smokers. In conclusion, rs878081 polymorphism of AIRE gene increases the risk of RA in a Chinese Han population.