Miracle-dz/UniFAR-FTUs huggingface dataset:
Pop Music Generation with Controllable Phrase Lengths

Research on music generation using deep learning has at-tracted more attention; in particular, Transformer-based models have succeeded in generating coherent musical pieces. Recently, an increasing number of studies have focused on phrases that are smaller musical units, and several studies have addressed phrase-level control. In this study, we propose a method for sequentially generating a piece that enables the control of each phrase length and, consequently, the length of the entire piece. We added P HRASE and a new event, B AR C OUNTDOWN , which indicates the number of bars remaining in the phrase, to the existing event-based music representations. To reﬂect user input indicating the phrase lengths of the piece being generated, we used an autoregressive generation model that adds these two events to the generated event-token sequence based on the user input and uses it as input for the next time step. Subjective listening tests revealed that the pieces generated by our methods possessed designated phrase lengths and ended naturally at the determined length. 1
POLYCORN: Data-driven Cross-layer Multipath Networking for High-speed Railway through Composable Schedulerlets

Modern high-speed railway (HSR) systems offer a speed of more than 250 km/h, making on-board Internet access through track-side cellular base stations extremely challenging. We conduct extensive measurements on commercial HSR trains, and collect a massive 1.79 TB GPS-labeled TCP-LTE dataset covering a total travel distance of 28,800 km. Leveraging the new insights from the measurement, we design, implement, and evaluate POLYCORN, a first-of-its-kind networking system that can significantly boost Internet performance for HSR passengers. The core design of POLYCORN consists of a suite of composable multipath schedulerlets that intelligently determine what, when, and how to schedule user traffic over multiple highly fluctuating cellular links between HSR and track-side base stations. POLYCORN is specially designed for HSR environments through a cross-layer and data-driven proactive approach. We deploy POLYCORN on the operational LTE gateway of the popular Beijing-Shanghai HSR route at 300 km/h. Real-world experiments demonstrate that POLYCORN outperforms the state-of-the-art multipath schedulers by up to 242% in goodput, and reduces the delivery time by 45% for instant messaging applications.
Minimal wearable setup via sensor correlation: a case study of field hockey players

Monitoring athletes with wearable sensors allows to gain insights on their technique and physical condition. However, invasive setups containing a large number of sensors may hinder the mobility of the athletes, leading to under-performance and possibly inaccurate data collection. In this paper, we show that correlation between data collected by different wearables can be used to identify a minimal setup. We propose a methodology to remove the least important sensors, and apply it to data collected by monitoring field hockey players. In this study, the number of sensors was reduced from 23 to 8 by deleting those exhibiting a correlation above 98% . Additionally, we demonstrate that even with a minimal sensor configuration, a significant amount of information is retained with regards to predicting the ball speed following a drag-flick, an important technique in field hockey. Our experiments indicate that the utility of the data for this specific task remains practically unaltered.
Scalable Distributed Trajectory Clustering Using Apache Spark

Trajectory clustering is an important problem, where position data of mobile objects, such as vehicles and vessels, is analyzed to extract knowledge utilized for a plethora of management tasks. Recently, a vast increase in the production of data gathering devices has taken place, allowing the collection of data in much larger volumes. This challenges the application of existing clustering algorithms, as they are not always able to handle large datasets due to their design. In particular, TRACLUS is one of the most well-known trajectory clustering algorithms that is a generalization of DBSCAN for trajectory line segments. However, due to the iterative approach and the repetitive usage of a spatial index inherited from DBSCAN, TRACLUS’s performance degrades as the datasets increase in size and can be extremely slow in some cases. To tackle this shortcoming, we propose a distributed implementation of TRACLUS, built on Apache Spark, that can operate on very large datasets by applying different types of partitioning to the input data. Results from an empirical evaluation on real-world trajectories illustrate that our distributed variant achieves improved runtime performance and clustering efficiency.
Outlier Detection and Cleaning in Trajectories: A Benchmark of Existing Tools

Outliers can affect trajectory analysis as they represent errors. There are two outlier detection categories, one focusing on a collection of trajectories, where one whole trajectory can be an outlier and another on points inside individual trajectories. In this paper, we focus on the latter. We benchmark existing open-source libraries and compare their efficiency and accuracy in cleaning outliers. To compare the accuracy, we present a method to build ground truth using the other sensor data in a multi-sensor environment.
Learning Confidence Intervals for Feature Importance: A Fast Shapley-based Approach

Inferring feature importance is a well-known machine learning problem. Giving importance scores to the input data features is particularly helpful for explaining black-box models. Existing approaches rely on either statistical or Neural Network-based methods. Among them, Shapley Value estimates are among the mostly used scores to explain individual classification models or ensemble methods. As a drawback, state-of-the-art neural network-based approaches neglects the uncertainty of the input predictions while computing the confidence intervals of the feature importance scores. The paper extends a state-of-the-art neural method for Shapley Value estimation to handle uncertain predictions made by ensemble methods and to estimate a confidence interval for the feature importances. The results show that (1) The estimated confidence intervals are coherent with the expectation and more reliable than baseline methods; (2) The efficiency of the Shapley value estimator is comparable to those of traditional models; (3) The level of uncertainty of the Shapley value estimates decreases while producing ensembles of larger numbers of predictors.
Conversational Requirements Engineering: Pinpointing Requirements-Relevant Information in Conversations

Discussions about system requirements, for instance interviews, analysis workshops and customer meetings, are essential in the process of gathering requirements and contribute significantly to the creation of requirements specifications. In the majority of cases, the tasks in this process – preparation, elicitation, note-taking, and post-processing – are manually performed by the practitioners. Consequently, important information could be overlooked or omitted in the requirements specification. The unavailability of data and the challenges associated with transcribing conversational data have resulted in very limited research until today. However, by leveraging recent advancements in meeting technology, such as Microsoft Teams, we conduct empirical research to investigate conversational artifacts. The resulting findings are then utilized in the design science-oriented development of solutions aimed at assisting practitioners in the requirements engineering process
RingLeader: Efficiently Offloading Intra-Server Orchestration to NICs

Careful orchestration of requests at a datacenter server is crucial to meet tight tail latency requirements and ensure high throughput and optimal CPU utilization. Orchestration is multi-pronged and involves load balancing and scheduling requests belonging to different services across CPU resources, and adapting CPU allocation to request bursts. Centralized intra-server orchestration offers ideal load balancing performance, scheduling precision, and burst-tolerant CPU re-allocation. However, existing software-only approaches fail to achieve ideal orchestration because they have limited scalability and waste CPU resources. We argue for a new approach that offloads intra-server orchestration entirely to the NIC. We present RingLeader, a new programmable NIC with novel hardware units for software-informed request load balancing and programmable scheduling and a new light-weight OS-NIC interface that enables close NIC-CPU coordination and supports NIC-assisted CPU scheduling. Detailed experiments with a 100 Gbps FPGA-based prototype show that we obtain better scalability, efficiency, latency, and throughput than state-ofthe-art software-only orchestrators including Shinjuku and Caladan.
ProximIoT: A Proximity-based Product Marketing Platform

The broad acceptance and deployment of smartphone and Internet of Things (IoT) technologies presents a first-class opportunity for strengthening product marketing via interaction between businesses and their in-store customers within physical stores. Knowledge and tracking of the physical proximity of customers to particular products can extend successful recommendation strategies for product marketing applied in online stores to physical stores. Thus, there is an opportunity for businesses to utilize the potential of smartphones and IoT for organizing advertising and promotion campaigns with better results compared to traditional techniques. This paper presents an IoT platform developed for proximity marketing, which collects information related to user locations in real time within the sales area and processed in correlation with historical data. The platform analyzes the aggregated data for optimal decision making by marketing departments and performs targeted interaction with consumers in real time through automated messaging. The IoT platform operation is based on Bluetooth beacon devices installed in the business sales area, combined with smartphones owned by customers. The aim of developing the ProximIoT platform is to improve the effectiveness of promotional activities put in place by businesses, as well as overall customer experience.
VERB: Visualizing and Interpreting Bias Mitigation Techniques Geometrically for Word Representations

Word vector embeddings have been shown to contain and amplify biases in the data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and attenuate these biases in word representations. In this paper, we utilize interactive visualization to increase the interpretability and accessibility of a collection of state-of-the-art debiasing techniques. To aid this, we present the Visualization of Embedding Representations for deBiasing (“VERB”) system, an open-source web-based visualization tool that helps users gain a technical understanding and visual intuition of the inner workings of debiasing techniques, with a focus on their geometric properties. In particular, VERB offers easy-to-follow examples that explore the effects of these debiasing techniques on the geometry of high-dimensional word vectors. To help understand how various debiasing techniques change the underlying geometry, VERB decomposes each technique into interpretable sequences of primitive transformations and highlights their effect on the word vectors using dimensionality reduction and interactive visual exploration. VERB is designed to target natural language processing (NLP) practitioners who are designing decision-making systems on top of word embeddings, and also researchers working with the fairness and ethics of machine learning systems in NLP. It can also serve as a visual medium for education, which helps an NLP novice understand and mitigate biases in word embeddings.
Secure File Transmission Scheme Based on Hybrid Encryption Technique

To enhance the security level of data transmission in Open Network, a hybrid encryption Technique based on AES, RSA and MD5 is proposed. The mechanism makes full use of advantage of AES RSA, MD5 because encryption speed of AES algorithm is faster than RSA algorithm for long Encrypting Plain Texts, and RSA algorithm Key distribute is very safely and easily. Under the dual protection with the AES algorithm and the RSA algorithm, the data transmission in the Open Network will be more secure. Mean while, it is clear that the procedure of the entire encryption is still simple and efficient as ever. Digital abstract Algorithm MD5 is adopted in this technique. It is an effective technique to overcome the problem of safe data transmission in network. This Technique maintained the confidentiality, Authentication and Integrity.
A deadzone compensator of a DC motor system using fuzzy logic control

A deadzone compensator is designed for a DC motor system using a fuzzy logic controller. The classification property of fuzzy logic systems makes them a natural candidate for the rejection of errors induced by the deadzone, which has regions in which it behaves differently. A tuning algorithm is given for the fuzzy logic parameters, so that the deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic deadzone compensator is implemented on a DC motor system to show its efficacy.
Eliminating the Interpolation Associated with the Semi-Lagrangian Scheme

Abstract There are several reasons why it is desirable to eliminate the interpolation associated with the conventional semi-Larangian scheme. Interpolation leads to smoothing and is also the most costly operation associated with the technique. Furthermore, its elimination produces a scheme that is more readily adaptable to a spectral model. In the conventional semi-Lagrangian method, in order to predict a field value at grid point (Xi, Yj) it is necessary to calculate the trajectory over one time step for the fluid element that arrives at (Xi, Yj). One then moves along this trajectory in order to extract the field value at an upstream location that generally lies between the grid points, and hence requires the use of interpolation formulae. This trajectory can be represented as a vector. In the new scheme, the trajectory vector is considered to be the sum of two other vectors—a first vector joining (Xi, Yj) to the grid point (Xu, Yu) nearest the upstream location, and a second vector joining (Xu, Yu) to t.
A Lie algebraic approach to Ricci flow invariant curvature conditions and Harnack inequalities

We consider a subset S of the complex Lie algebra soðn;CÞ and the cone CðSÞ of curvature operators which are nonnegative on S. We show that CðSÞ defines a Ricci flow invariant curvature condition if S is invariant under AdSOðn;CÞ. The analogue for Kahler curvature operators holds as well. Although the proof is very simple and short, it recovers all previously known invariant nonnegativity conditions. As an application we reprove that a compact Kahler manifold with positive orthogonal bisectional curvature evolves to a manifold with positive bisectional curvature and is thus biholomorphic to CP n . Moreover, the methods can also be applied to prove Harnack inequalities.
Staging for general purpose languages

Reliable and easy to maintain source code rarely yields eﬃ-cient target programs. Conversely, eﬃcient code is usually hard to write, hard to maintain and prone to errors. One way to retain both good programming practices and optimal performance is to write a high-level program generator that outputs an eﬃcient, specialized target program. Staging is an approach that combines code generation and target instructions in a single meta-program, by delaying the evaluation of target expressions. LMS (lightweight modular staging) is a library-based framework that acts as an embedded compiler for such meta-programs. It is particularly well suited for embedded domain-speciﬁc languages, which can be built on top of the framework. However, general purpose languages require a diﬀerent approach, since they are typically not restricted to a superset of another language. The approach presented in this paper is to extend LMS with a frontend that maps a small subset of Scala to LMS intermediate representation, and a backend that generates x86 instructions from this representation. This allows to eﬀectively use LMS as a compiler for a restricted general purpose language. Future work could extend this approach to more expressive general purpose languages.
Development of method for anti-icing materials consumption optimization during winter road maintenance

The object of research is the processes of managing the consumption of anti-icing materials during the winter maintenance of highways. The main hypothesis of the research is the application of mathematical modelling methods to optimize the consumption of anti-icing materials. The study considered the factors that affect the use of both individual types and anti-icing materials as a whole. It was determined that the temperature of the road surface and the selected winter maintenance strategy taking into account the level of service (LOS) are significant factors. A mathematical model of material consumption optimization based on the binary numbering system is proposed. Additional limitations to the model have been established: compliance with the criteria of reliability, preservation of assets, road safety, compliance with user expectations (social factor), environmental friendliness, efficiency of the use of materials. An algorithm for the application of the method of optimizing the consumption of anti-icing materials during the winter maintenance of highways has been developed. It was determined that the developed method solves the main problem of minimizing the costs of anti-icing materials, taking into account the requirements of the level of service, the chosen strategy, the temperature of the road surface and the criteria of impact on winter maintenance. It is proposed to implement the calculation according to this method using the built-in «Parameter selection» function in the Microsoft Excel license environment (USA). The results obtained by the optimization method, based on real data, showed the possibility of saving materials by about 23 %, which confirms the effectiveness of the proposed solutions. The results of calculations based on the method of optimization of material consumption have practical value and can be used to make management decisions regarding the justification of winter road maintenance operations. The proposed method can be used both for the cost planning phase and in the case of analysis in the post-implementation period. This is an important aspect in the conditions of financial and resource uncertainty, including those related to circumstances of force majeure, in particular, martial law in Ukraine.
miRNA-1 promotes acute myeloid leukemia cell pathogenesis through metabolic regulation

Acute myeloid leukemia (AML) is a heterogeneous and deadly disease characterized by uncontrolled expansion of malignant blasts. Altered metabolism and dysregulated microRNA (miRNA) expression profiles are both characteristic of AML. However, there is a paucity of studies exploring how changes in the metabolic state of the leukemic cells regulate miRNA expression leading to altered cellular behavior. Here, we blocked pyruvate entry into mitochondria by deleting the Mitochondria Pyruvate Carrier (MPC1) gene in human AML cell lines, which decreased Oxidative Phosphorylation (OXPHOS). This metabolic shift also led to increased expression of miR-1 in the human AML cell lines tested. AML patient sample datasets showed that higher miR-1 expression correlates with reduced survival. Transcriptional and metabolic profiling of miR-1 overexpressing AML cells revealed that miR-1 increased OXPHOS, along with key metabolites that fuel the TCA cycle such as glutamine and fumaric acid. Inhibition of glutaminolysis decreased OXPHOS in miR-1 overexpressing MV4-11 cells, highlighting that miR-1 promotes OXPHOS through glutaminolysis. Finally, overexpression of miR-1 in AML cells exacerbated disease in a mouse xenograft model. Together, our work expands current knowledge within the field by uncovering novel connections between AML cell metabolism and miRNA expression that facilitates disease progression. Further, our work points to miR-1 as a potential new therapeutic target that may be used to disrupt AML cell metabolism and thus pathogenesis in the clinic. Graphical Abstract We blocked pyruvate entry into mitochondria and decreased Oxidative Phosphorylation (OXPHOS) in human AML cell lines MV4-11 and MOLM-14 by using gene editing tools. This metabolic shift led to increased expression of miR-1 in the human AML cell lines. TARGET and TCGA AML patient sample dataset analysis revealed that miR-1 is overexpressed in patients with FLT3-ITD + mutation, and independently from FLT3-ITD, high levels of miR-1 also correlates with reduced survival. Transcriptional and metabolic profiling of miR-1 overexpressing AML cells revealed that miR-1 promotes OXPHOS through glutaminolysis. Moreover, the overexpression of miR-1 in AML cells exacerbated disease in a mouse xenograft model. Together, our work expands current knowledge within the field by uncovering novel connections between AML cell metabolism and miRNA expression that facilitates disease progression. Created with BioRender.com.
A recent distemper virus outbreak in the growing canine populations of Galapagos Islands: a persistent threat for the endangered Galapagos Sea Lion

Canine Distemper Virus (CDV) is a highly contagious virus that can cross mammalian species barriers and has widespread impacts on both domestic animals and wildlife populations. This study describes a recent outbreak of CDV in the Galapagos Islands in 2019. A total number of 125 dogs with clinical signs compatible with CDV were included in this study. Nasal swabs were taken and analyzed by RT-qPCR for the detection of CDV, resulting in a positivity rate of 74.4% (IC95%, 66–81%). Among the CDV positive dogs, 82.2% presented with respiratory signs, 48.8% neurological signs, and 28.9% gastrointestinal signs. CDV has been previously reported in the domestic canine population of the Galapagos Islands in 2001 and 2004. The current study shows how CDV is still a threat for the endemic and endangered Galapagos sea lion, despite recent policies for dog population control and CDV vaccination.