Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing Gender Bias in NMT with FUDGE

Gender bias appears in many neural machine translation (NMT) models and commercial translation software. Research has become more aware of this problem in recent years and there has been work on mitigating gender bias. However, the challenge of addressing gender bias in NMT persists. This work utilizes a controlled text generation method, Future Discriminators for Generation (FUDGE), to reduce the so-called Speaking As gender bias. This bias emerges when translating from English to a language that openly marks the gender of the speaker. We evaluate the model on MuST-SHE, a challenge set to specifically evaluate gender translation. The results demonstrate improvements in the translation accuracy of the feminine terms.
DataLitMT – Teaching Data Literacy in the Context of Machine Translation Literacy

This paper presents the DataLitMT project conducted at TH Koln – University of Applied Sciences. The project develops learning resources for teaching data literacy in its translation-specific form of professional machine translation (MT) literacy to students of translation and specialised communication programmes at BA and MA levels. We discuss the need for data literacy teaching in a translation/specialised communication context, present the three theoretical pillars of the project (consisting of a Professional MT Literacy Framework, an MT-specific data literacy framework and a competence matrix derived from these frameworks) and give an overview of the learning resources developed as part of the project.
Terminology in Neural Machine Translation: A Case Study of the Canadian Hansard

Incorporating terminology into a neural machine translation (NMT) system is a feature of interest for many users of machine translation. In this case study of English-French Canadian Parliamentary text, we examine the performance of standard NMT systems at handling terminology and consider the tradeoffs between potential performance improvements and the efforts required to maintain terminological resources specifically for NMT.
MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages

We present the most relevant results of the project MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages in its second year. To date, parallel and monolingual corpora have been produced for seven low-resourced European languages by crawling large amounts of textual data from selected top-level domains of the Internet; both human and automatic evaluation show its usefulness. In addition, several large language models pretrained on MaCoCu data have been published, as well as the code used to collect and curate the data.
Segment-based Interactive Machine Translation at a Character Level

To produce high quality translations, human translators need to review and correct machine translation hypothesis in what it is known as post-editing. In order to reduce the human effort of this process, interactive machine translation proposed a collaborative framework in which human and machine work together to generate the translations. Among the many protocols proposed throughout the years, the segment-based one established a paradigm in which the post-editor was allowed to validate correct word sequences from a translation hypothesis and introduced a word correction to help the system improve the next hypothesis. In this work we propose an extension to this protocol: instead of having to the type the complete word correction, the system will complete the user’s correction while they are typing. We evaluated our proposal under a simulated environment, achieving a significant reduction of the human effort.
SSVEPs reveal dynamic (re-)allocation of spatial attention during maintenance and utilization of visual working memory

Visual Working Memory (VWM) allows us to temporarily store goal-relevant information to guide future behavior. Prior work has established that VWM and spatial attention are intrinsically connected – even if location is irrelevant for responses: behavioral and neural correlates of attention show spatial biases, specific to the location at which memory items were encoded. This suggests that VWM is spatially organized and that maintaining information might rely on the allocation of spatial attention towards the location of memory items. Importantly, attention often needs to be dynamically redistributed between several locations, e.g. in preparation for an upcoming probe. Very little is known about how attentional resources are distributed between multiple locations during a VWM task and even less about the dynamic changes governing such attentional shifts over time. This is largely due to the inability to use behavioral outcomes to reveal fast dynamic changes within trials. We here demonstrate EEG Steady-State Visual Evoked Potentials (SSVEPs) to successfully track the dynamic allocation of spatial attention during a VWM task. Participants were presented with to-be-memorized gratings and distractors at two distinct locations. During maintenance and retrieval, each location was tagged with a disk flickering at either 10 or 13 Hz. This allowed us to dynamically track attention allocated to memory and distractor items via their coupling with space by quantifying the amplitude and coherence of SSVEP responses in the EEG signal to flickering stimuli at the former memory and distractor locations. SSVEP responses at memory locations did not differ from distractor locations during early parts of the maintenance window. However, shortly before probe comparison, we observed a decrease in SSVEP coherence over distractor locations indicative of a reallocation of spatial attentional resources. Reaction times were shorter when preceded by stronger decreases in SSVEP coherence at distractor locations, reflecting the reallocation of attention from the distractor location to the memory location or towards the distinct upcoming probe location. Broader Significance We demonstrate that SSVEPs can inform about dynamic processes in VWM, even if location does not have to be reported by participants. This finding not only supports the notion of a spatially organized VWM, but also reveals that SSVEPs betray a dynamic prioritization process of working memory items and locations over time that is directly predictive of memory performance.
Sex- versus apomictic-specific polymorphisms in the 5’UTR of APOLLO from Boechera shift gene expression from somatic to reproductive tissues in Arabidopsis

Among candidate genes underlying the control components of apomixis, APOLLO is known for its strong linkage to apomeiosis in the genus Boechera. The gene has “apo-alleles”, which are characterized by a set of linked apomixis-specific polymorphisms, and “sex-alleles”. All apomictic□Boechera genotypes are heterozygous for the apo/sex-alleles, while all sexual genotypes are homozygous for sex-alleles. In this study, native and synthetic APOLLO promoters were characterized by detecting the expression level of the β-glucuronidase (GUS) gene in Arabidopsis. Comparing various flower developmental stages in transgenic lines containing different constructs with 2 kb native transgenic lines revealed that changes to the APOLLO promoter causes shifts in tissue- and developmental-stage specificity of GUS expression. Importantly, several apomixis-specific polymorphisms in the 5’UTR change the timing and location of GUS activity from somatic to reproductive tissues. These synthetic data simulate a plausible evolutionary process whereby apomixis-specific gene activity can be achieved.
PTPN2 copper-sensing rapidly relays copper level fluctuations into EGFR/CREB activation and associated CTR1 transcriptional repression

Fluxes in human intra- and extracellular copper levels recently garnered attention for roles in cellular signaling, including affecting levels of the signaling molecule cyclic adenosine monophosphate (cAMP). We herein applied an unbiased temporal evaluation of the whole-genome transcriptional activities modulated by fluctuations in copper levels to identify the copper sensor proteins responsible for driving these activities. We found that fluctuations in physiologically-relevant copper levels rapidly modulate EGFR/MAPK/ERK signal transduction and activation of the transcription factor cAMP response element-binding protein (CREB). Both intracellular and extracellular assays support Cu1+ inhibition of the EGFR-phosphatase PTPN2 (and potentially the homologous PTPN1)–via direct ligation to the PTPN2 active site cysteine side chain–as the underlying mechanism of copper-stimulated EGFR signal transduction activation. Depletion of copper represses this signaling pathway. We additionally show i) copper supplementation drives transcriptional repression of the copper importer CTR1 and ii) CREB activity is inversely correlated with CTR1 expression. In summary, our study reveals PTPN2 as a physiological copper sensor and defines a regulatory mechanism linking feedback control of copper-stimulated MAPK/ERK/CREB-signaling and CTR1 expression, thereby uncovering a previously unrecognized link between copper levels and cellular signal transduction.
The early communication stages between serine proteases and enterovirus capsids in the race for viral disintegration

Enteroviruses are human pathogens known to challenge water safety1,2. Among the microbial stressors found in water, bacterial serine proteases contribute to the control of enterovirus persistence3. However, the structural interactions accounting for the susceptibility of enteroviruses to proteases remains unexplained. Here, we describe the molecular mechanisms involved in the recruitment of serine proteases to viral capsids. Among the virus types used, coxsackievirus A9 (CVA9), but not CVB5 and echovirus 11 (E11), was inactivated by Subtilisin A in a host-independent manner, while Bovine Pancreatic Trypsin (BPT) only reduced CVA9 infectivity in a host-dependent manner. Predictive interaction models of each protease with capsid protomers indicate the main targets as internal disordered protein (IDP) segments exposed either on the 5-fold vertex (DE loop VP1) or at the 5/2-fold intersection (C-terminal end VP1) of viral capsids. We further show that a functional binding protease/capsid depends on both the strength and the evolution over time of protease-VP1 complexes, and lastly on the local adaptation of proteases on surrounding viral regions. Finally, we identified three residues on CVA9 capsid that trigger cleavage by Subtilisin A, one of which acts as a sensor residue contributing to enzyme recognition on the DE loop. Overall, this study describes an important biological mechanism involved in enteroviruses biocontrol.
Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air

Here is disclosed a method of manufacturing building blocks filled with a cured aminoplast resin foam by forming a curable foam, presenting the cavity to and in sealing relationship with one end of a delivery pipe for the foam and injecting the curable foam through the delivery pipe into the cavity until a back pressure is developed in the delivery pipe. Apparatus for manufacturing the building blocks is also described.
Riksinventering 2000 : En synoptisk studie av vattenkemi och bottenfauna i svenska sjöar och vatten

Inventeringen ar 2000 omfattade ca 3400 sjoar och 700 vattendrag. I huvudsak provtogs samma objekt som vid riksinventeringen 1995. Prover for vattenkemi togs i alla objekt, medan bottenfaunaprover .
Determination of 17β-estradiol and Estrone Concentrations in Runoff and Topsoil from Plots Receiving Dairy Manure

I am submitting herewith a thesis written by Renea Dyer entitled “Determination of 17βestradiol and Estrone Concentrations in Runoff and Topsoil from Plots Receiving Dairy Manure. ” I have examined the final electronic copy of this thesis for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Master of Science, with a major in Biosystems Engineering Technology.
Safety and cost‐effectiveness of shortening hospital follow‐up after breast cancer treatment

In the Netherlands, the first 5 years of follow‐up after treatment for breast cancer are carried out in hospital with yearly mammography. After this, for patients aged over 60 years who have undergone mastectomy, there is a shift of care to the National Screening Programme (NSP) for mammography every 2 years. After breast‐conserving therapy follow‐up is perfomed by the general practitioner (GP), with mammography every second year and physical examination annually. The aim of this study was to evaluate the clinical effects and costs of four different strategies for follow‐up after breast cancer treatment.
Malware Speaks! Deep Learning Based Programming Language Processing for Detecting Evasive Cyberattacks

The autonomous detection of cyberattacks is an important and non-trivial research problem. Especially, evasive cyberattacks are becoming increasingly sophisticated in hiding their presence by obfuscating their attack steps e.g. file-less malware. In particular, evasive Advanced Persistent Threats (APTs) can stay in the system undetected for a long period of time. After gaining access to the system, APTs can establish system channels and backdoors where they can operate without being detected. They observe the sys-tem defenses, learn, and continuously evolve. They can execute their attacks–corrupting system’s behavior—slowly, leaving long intervals between attack steps thus avoiding detection e.g. Kernel Rootkits and Return-oriented programming (ROP). Due to the long intervals between attack steps, it is difficult to trace and correlate them, even for defense mechanisms based on machine learning models. There is also a new class of evasive APTs that just use the victim’s computational resources without their authorization e.g. cryptomining malware or encrypt the files in the system to demand ransom for decrypting them e.g. Ransomware. Detecting cryptomining and ransomware is difficult since they mimic benign applications such as compression applications’ behavior by performing similar bitwise, cryptographic, and encryption operations. In this paper, we propose processing of assembly language—a fundamental and platform-independent programming language—as a natural language using deep learning for understanding and profiling applications. Our solution leverages the attackers’ immutable action: deploying instructions in system’s memory in order to carry out the attack. We sample and extract these assembly instructions from memory for training and detecting evasive cyberattacks. We use cryptomining malware as a case study for this work. We introduce new principles for applying deep learning to detect evasive malware based on the instructions they execute i.e. communication between malware and the system. We introduce a new representation of assembly instructions, extracted from the memory for each application: Assembly Code Sets (ACSs). ACSs are sequences of instructions that are systematically and contextually related to each other. This leads us to the design and development of deep learning based application profiling model called Deep Code Profiler (De-Code Pro). Through extensive experiments, we show that DeCode Pro is highly effective in the classification of cryptomining malware and incur significantly low false positive and false negative rates. We also show that the model achieves high classification accuracy even with limited training data, which can considerably reduce the computing resources required for training and retraining the deep learning model.
Evaluation of Regions of Interest in Voicebot Q&A System

- Voice-activated question-and-answer (Q&A) systems have witnessed significant advancements in recent years, transforming the way humans interact with technology. In this study, we present a novel approach to enhance the accuracy and efficiency of voicebot Q&A systems by identifying and evaluating Regions of Interest (ROIs) within the input voice data using Support Vector Machines (SVM). The proposed method involves the use of SVM, a robust machine learning algorithm, to classify distinct ROIs in voice inputs, enabling the system to focus on the most relevant parts of the user's query in a university's conversational agent. By capturing key ROIs, the voicebot Q&A system can streamline the processing pipeline, leading to faster and more accurate responses to user inquiries. To evaluate the performance of our approach extensive experiments based on the SVM algorithm and other deep neural network-based architectures. The results of SVM-based ROIs identification against other approaches, assessing accuracy, and other statistical parameters have been computed and analyzed. The influence of different SVM configurations on the overall performance of the system has also been analyzed. The experimental results demonstrate that the integration of SVM for ROI identification significantly improves the voicebot Q&A system's performance compared to traditional methods. The use of ROIs reduces processing overhead, enabling the system to deliver responses with enhanced precision and efficiency. Furthermore, user feedback indicates improved satisfaction with the voicebot's capabilities, thereby enhancing the overall user experience. In conclusion, this study highlights the efficacy of employing SVM-based ROIs evaluation in voicebot Q&A systems, presenting a promising direction for future advancements in natural language processing and voice-activated technologies. The proposed method offers valuable insights into refining voicebot performance, catering to a wide array of user requirements and preferences across different application
Offline approach towards obstacle avoidance in collaborative robots

: Performance improvement in Human-Robot Collaboration is one of the objectives in collaborative robots. This improvement can be achieved through the correct obstacle avoidance planification of collision-free paths, focusing on terms of path continuity, final motion times and average speed of the end-effector. We propose an offline path planning approach considering an obstacle given an initial linear trajectory from pose A to pose B. By using ROS, MoveIt! and Rviz we generate three trajectories varying the sagitta of our equations for evaluation and determining the best performing trajectory. Results in simulations are compared with the actual motions using the UR10e cobot.
The Economics of Density: Evidence from the Berlin Wall

This paper develops a quantitative model of internal city structure that features agglomeration and dispersion forces and an arbitrary number of heterogeneous city blocks. The model remains tractable and amenable to empirical analysis because of stochastic shocks to commuting decisions, which yield a gravity equation for commuting flows. To structurally estimate agglomeration and dispersion forces, we use data on thousands of city blocks in Berlin for 1936, 1986 and 2006 and exogenous variation from the city’s division and reunification. We estimate substantial and highly localized production and residential externalities. We show that the model with the estimated agglomeration parameters can account both qualitatively and quantitatively for the observed changes in city structure. We show how our quantitative framework can be used to undertake counterfactuals for changes in the organization of economic activity within cities in response for example to changes in the transport network.
Aerosol production in the surf zone and effects on IR extinction

Aerosol concentrations over the surf were measured during the EOPACE (Electro-Optical Propagation Assessment in Coastal Environment) Surf-i experiment in La Jolla, California. Particle size distributions were measured on the beach (at three levels) and across the surf (one level). Concentrations of droplets smaller than i im in diameter are little affected by the surf, while those with diameters in the 1-10 im range increase by up to two orders of magnitude. Clear vertical gradients were observed, which vary with particle size. No relation could be established between the surf production and wind speed or wave properties. Extinction coefficients at visible and infrared wavelengths calculated from the particle size distributions show that these are enhanced by a factor of 30 to 100, depending on the wavelength. Using the measured concentrations as boundary condition, calculations with a simple dispersion model show the gradual decrease in the concentration with fetch in off-shore winds. In on-shore winds the surf-enhanced aerosol concentration is effective over only a short range, but nevertheless significant transmission losses may occur. Obviously, these conclusions apply only to the surf encountered during this specific experiment. The effects of the surf in other areas and other ambient conditions will be assessed from the analysis of data collected at a different location and in different conditions.
Facial Emotion Detection Using Neural Network

-Facial expression recognition is a rapidly growing field within pattern recognition and computer vision, finding applications in diverse areas such as artificial intelligence, human-computer interaction and security monitoring. Its significance has increased in recent years due to its potential in various domains. The project investigates the effectiveness of pretrained CNN model weights for facial expression (FER) and explores their potential for emotion based recommendations. By transferring learned weights from a pretrained CNN model trained on a large-scale FER dataset, the model chives improved accuracy and efficiency. Fine tuning with a smaller FER dataset further enhances adaptability.
COMPARISON OF OPEN POLLINATED VARIETIES AND NEWLY DEVELOPED HYBRIDS FOR YIELD AND YIELD CONTRIBUTING TRAITS IN MAIZE

Eighteen hybrids and 13 open pollinated varieties of maize were evaluated at the National Agricultural Research Centre, Islamabad during kharif 2007. Significant differences were observed for days to 50% tasseling and silking, plant height, ear height, number of kernel rows per ear, number of grain per row, 100 grain weight, grain moisture and grain yield. The hybrids NT-6622 and NT- 6651 ranked top and second in grain yield by producing 7842 and 7759 kg ha -1 , respectively. Generally the hybrids produced more grain yield than the open polli- nated varieties. Days to 50% tasseling ranged from 47.33 (EV-1098) to 64 (NT- 6632) while for silking varied from 47.67 (EV-1098) to 63.33 (30-K-95). The variety Soan-3 (149 cm) was the shortest and hybrid 30-K-95 (202.3 cm) was the tallest amongst all the varieties and hybrids. Ear height ranged from 70.33 (Soan-3) to 107 cm (NT-6651) while number of kernel rows per ear varied from 12 (NT-6622, 30- K95, 2512 and 2514) to 18 (R-2207). The hybrid P-30-25 produced the maximum number of grains (51) Grast-8288 produced the lowest (29). One hundred grain weight ranged from 23g (EV-6098) to 39g (2512).