Miracle-dz/UniFAR-FTUs huggingface dataset:
Runtime energy management of concurrent applications for multi-core platforms

Multi-core platforms are employing a greater number of heterogeneous cores and resource configurations to achieve energy-efficiency and high performance. These platforms often execute applications with different performance constraints concurrently, which contend for resources simultaneously, thereby generating varying workload and resources demands over time. There is a little reported work on runtime energy management of concurrent execution, focusing mostly on homogeneous multi-cores and limited application scenarios. This thesis considers both homogeneous and heterogeneous multi-cores and broadens application scenarios. The following contributions are made in this thesis. Firstly, this thesis presents online Dynamic Voltage and Frequency Scaling (DVFS) techniques for concurrent execution of single-threaded and multi-threaded applications on homogeneous multi-cores. This includes an experimental analysis and deriving metrics for efficient online workload classification. The DVFS level is proactively set through predicted workload, measured through Memory Reads Per Instruction. The analysis also considers thread synchronisation overheads, and underlying memory and DVFS architectures. Average energy savings of up to 60% are observed when evaluated on three different hardware platforms (Odroid-XU3, Intel Xeon E5-2630, and Xeon Phi 7620P). Next, an energy efficient static mapping and DVFS approach is proposed for heterogeneous multi-core CPUs. This approach simultaneously exploits different types of cores for each application in a concurrent execution scenario. It first selects performance meeting mapping (no. of cores and type) for each application having minimum energy consumption using offline results. Then online DVFS is applied to adapt to workload and performance variations. Compared to recent techniques, the proposed approach has an average of 33% lower energy consumption when validated on the Odroid-XU3. To eliminate dependency on the offline application profiling and to adapt to dynamic application arrival/completion, an adaptive mapping approach coupled with DVFS is presented. This is achieved through an accurate performance model, and an energy efficient resource selection technique and a resource manager. Experimental evaluation on the Odroid-XU3 shows an improvement of up to 28% in energy efficiency and 7.9% better prediction accuracy by performance models.
Multi-Objective Optimization of FM Noise Waveforms via Generalized Frequency Template Error Metrics

FM noise waveforms have been experimentally demonstrated to achieve high time bandwidth products and low autocorrelation sidelobes while achieving acceptable spectral containment in physical implementation. Still, it may be necessary to further reduce sidelobe levels for detection or improve spectral containment in the face of growing spectral use. The Frequency Template Error (FTE) and the Logarithmic Frequency Template Error (Log-FTE) metrics were conceived as means to achieve FM noise waveforms with good spectral containment and good autocorrelation sidelobes. In practice, FTE based waveform optimizations have been found to produce better autocorrelation responses at the expense of spectral containment while Log-FTE optimizations achieve excellent spectral containment and interference rejection at the expense of autocorrelation sidelobe levels. In this work, the notion of the FTE and LogFTE metrics are considered as subsets of a broader class of frequency domain metrics collectively termed as the Generalized Frequency Template Error (GFTE). In doing so, many different P-norm based variations of the FTE and Log-FTE cost functions are extensively examined and applied via gradient descent methods to optimize polyphasecoded FM (PCFM) waveforms. The performance of the different P-norm variations of the FTE and Log-FTE cost functions are compared amongst themselves, against each other, and relative to a previous FM noise waveform design approach called PseudoRandom Optimized FM (PRO-FM). They are evaluated in terms of their autocorrelation sidelobes, spectral containment, and their ability to realize spectral notches within the 3 dB bandwidth for the purpose of interference rejection. These comparisons are performed in both simulation and experimentally in loopback where it was found that
IoT Based Sign Language Interpretation System

In India about 31% of peoples are deaf or mute. One third of them are both deaf and mute. Gesture is a simple and important mode of interpersonal communication, which can simulate language and behaviour to express particular feelings and words. In this paper we have proposed a method that gives a method for improving Sign Language Interpretation system. In this method, we will use flex sensors along with pressure sensor which are incorporated on a glove to detect the gestures and convert it to text with in an Android Smart Phone. The text-to-speech module is also added in android application to improve the way of communication for deaf and mute peoples.
SplashCode - A Board Game for Learning an Understanding of Algorithms in Middle School

Teaching computational thinking in K-12 as a 21th century skill is becoming increasingly important. Computational thinking describes a specific way of reasoning building on concepts and processes derived from algorithms and programming. One way to teach these concepts is games as an effective and efficient alternative. This article presents SplashCode, a low-cost board game to reinforce basic algorithms and programming concepts. The game was developed in a systematic way following an instructional design process, and applied and evaluated in a Brazilian public school with a total of 65 students (grade 5 to 9). First results indicate that the game can have a positive impact on motivation, learning experience, and students’ learning, as well as contribute positively to social interaction, relevance, and fun. Results of this study may assist in the selection of games as an instructional strategy and/or in the development of new games for teaching computational thinking. (Preprint, Informatics in Education, 2019.
Evolving optimal learning strategies for robust locomotion in the spring-loaded inverted pendulum model

Robust locomotion in a wide range of environments is still beyond the capabilities of robots. In this article, we explore how exploiting the soft morphology can be used to achieve stability in the commonly used spring-loaded inverted pendulum model. We evolve adaption rules that dictate how the attack angle and stiffness of the model should be changed to achieve stability for both offline and online learning over a range of starting conditions. The best evolved rules, for both the offline and online learning, are able to find stability from a significantly wider range of starting conditions when compared to an un-adapting model. This is achieved through the interplay between adapting both the control and the soft morphological parameters. We also show how when using the optimal online rule set, the spring-loaded inverted pendulum model is able to robustly withstand changes in ground level of up to 10 m downwards step size.
Efficient processing of GRU based on word embedding for text classification

Text classification has become very serious problem for big organization to manage the large amount of online data and has been extensively applied in the tasks of Natural Language Processing (NLP). Text classification can support users to excellently manage and exploit meaningful information require to be classified into various categories for further use. In order to best classify texts, our research efforts to develop a deep learning approach which obtains superior performance in text classification than other RNNs approaches. However, the main problem in text classification is how to enhance the classification accuracy and the sparsity of the data semantics sensitivity to context often hinders the classification performance of texts. In order to overcome the weakness, in this paper we proposed unified structure to investigate the effects of word embedding and Gated Recurrent Unit (GRU) for text classification on two benchmark datasets included (Google snippets and TREC). GRU is a well-known type of recurrent neural network (RNN), which is ability of computing sequential data over its recurrent architecture. Experimentally, the semantically connected words are commonly near to each other in embedding spaces. First, words in posts are changed into vectors via word embedding technique. Then, the words sequential in sentences are fed to GRU to extract the contextual semantics between words. The experimental results showed that proposed GRU model can effectively learn the word usage in context of texts provided training data. The quantity and quality of training data significantly affected the performance. We evaluated the performance of proposed approach with traditional recurrent approaches, RNN, MV-RNN and LSTM, the proposed approach is obtained better results on two benchmark datasets in the term of accuracy and error rate.
Towards an on-chip power supply: Integration of micro energy harvesting and storage techniques for wireless sensor networks

The lifetime of a power supply in a sensor node of a wireless sensor network is the decisive factor in the longevity of the system. Traditional Li-ion batteries cannot fulfill the demands of sensor networks that require a long operational duration. Thus, we require a solution that produces its own electricity from its surrounding and stores it for future utility. Moreover, as the sensor node architecture is developed on complimentary metal-oxide-semiconductor technology (CMOS), the manufacture of the power supply must be compatible with it. In this thesis, we shall describe the components of an on-chip lifetime power supply that can harvest the vibrational mechanical energy through piezoelectric microcantilevers and store it in a reduced graphene oxide (rGO) based microsupercapacitor, and that is fabricated through CMOS compatible techniques. Our piezoelectric microcantilevers confirm the feasibility of fabricating micro electro- mechanical-systems (MEMS) size two-degree-of-freedom systems which can solve the major issue of small bandwidth of piezoelectric micro-energy harvesters. These devices use a cut-out trapezoidal cantilever beam to enhance the stress on the cantilever’s free end while reducing the gap remarkably between its first two eigenfrequencies in 400 - 500 Hz and 1 - 2 kHz range. The energy from the M-shaped harvesters will be stored in rGO based microsupercapacitors. These microsupercapacitors are manufactured through a fully CMOS compatible, reproducible, and reliable micromachining processes. Furthermore, we have also demonstrated an improvement in their electrochemical performance and yield of fabrication through surface roughening from iron nanoparticles. We have also examined the possibility of integrating these devices into a power management unit to fully realize a lifetime power supply for wireless sensor networks.
Cross-Domain Training for Goal-Oriented Conversational Agents

Goal-Oriented Chatbots in fields such as customer support, providing certain information or general help with bookings or reservations, suffer from low performance partly due to the difficulty of obtaining large domain-specific annotated datasets. Given that the problem is closely related to the domain of the conversational agent and the data belonging to a specific domain is difficult to annotate, there have been some attempts at surpassing these challenges such as unsupervised pre-training or transfer learning between different domains. A more thorough analysis of the transfer learning mechanism is justified by the significant improvement of the results demonstrated in the results section. We describe extensive experiments using transfer learning and warm-starting techniques with improvements of more than 5% in relative percentage of success rate in the majority of cases, and up to 10x faster convergence as opposed to training the system without them.
An Effective Metaheuristic for Multiple Traveling Repairman Problem with Distance Constraints

Multiple Traveling Repairman Problem with Distance Constraints (MTRPD) is an extension of the NP-hard Multiple Traveling Repairman Problem. In MTRPD, a ﬂeet of identical vehicles is dispatched to serve a set of customers with the following constraints. First, each vehicle’s travel distance is limited by a threshold. Second, each customer must be visited exactly once. Our goal is to ﬁnd the visiting order that minimizes the sum of waiting times. To solve MTRPD we propose to combine the Insertion Heuristic (IH), Variable Neighborhood Search (VNS), and Tabu Search (TS) algorithms into an eﬀective two-phase metaheuris-884
Sanctions on digital platforms: Balancing proportionality in a modern public square

Abstract This paper asks which legal tools digital operators could use to manage colliding rights on their platforms in a digitalised and transnational space such as the Internet. This space can be understood as a “modern public square”, bringing together actions in the digitalised world and their interactions with actual events in the physical world. It is then useful to provide this space with a discursive framework allowing for discussing and contesting actions happening on it. In particular, this paper suggests that two well-known legal concepts, proportionality and sanctions, can be helpfully articulated within that discursive framework. In a first step, proportionality, a justificatory tool, is often used to suggest a way for managing colliding rights. This paper argues that for proportionality to be useful in managing colliding rights on digital platforms, its role, scope and limits need to be better framed and supplemented by an overall digital environment which can feed into the proportionality test in an appropriate way. This can be provided, thanks to a second step, namely labelling in law the actions digital operators take as sanctions. Sanctions are the reactions organised by digital operators to bring back social order on the platforms. The labelling of these reactions under the legal category of “sanctions” offers a meaningful tool for thinking about what digital operators do when they manage colliding rights by blocking or withdrawing contents and/or accounts. As different types of sanctions can be distinguished, differentiated legal consequences, especially in relation to managing colliding rights, can be identified. Here the role played by the proportionality test can be distinguished depending on the type of sanctions. In any case, for sanctions and proportionality to help address colliding rights on the modern public square, a discursive framework needs to be developed, which depends on the existence of relevant meaningful communities engaging in reflecting on the use of sanctions and proportionality.
Optimization of Basic Clustering for Ensemble Clustering: An Information-Theoretic Perspective

The current research on ensemble clustering mainly focuses on integration strategies, but the attention regarding the measurement and optimization of basic cluster is less emphasized. Based on the information entropy theory, this paper proposes a quality metric of basic cluster, and the clusterings are further selected by incorporating two-branch decisions and three-way decisions respectively. Determined by preset threshold(s), mechanism of two-branch based basic clustering filtering (BCF2BD) and three-way based basic clustering filtering (BCF3WD) are developed. Concretely, the basic clustering in BCF2BD is deleted if the quality metric of it is less than the preset threshold <inline-formula> <tex-math notation="LaTeX">$\xi $ </tex-math></inline-formula>, and the new clustering member is added to maintain the basic cluster set count. The basic clustering in BCF3WD is deleted if the quality metric of it is less than the preset threshold <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>, retained if the quality metric of it is greater than the preset threshold <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, recalculated if the quality metric of it is greater than <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> and less than <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>. Both mechanism executed repeatedly until either non-decrement of basic clusters occurred or maximum iteration count reached. Contrastive experiments show that both methods of filtering algorithms can effectively improve the performance of ensemble clustering, and the three-way decisions filtering algorithm get less time consumption.
An Improved Algorithm of Parameter Kernel Cutting Based on Complex Fusion Image

— Aiming at the problem that the color image with more detailed textures is not highly segmented in the image segmentation process, a PKGC image segmentation method based on improved edge detection difference ratio is proposed. The method first constructs an energy function by using a parametric kernel graph cutting algorithm. Then, the value of the three-channel RGB edge detection ratio of the color image is used to change the constant balance factor in the energy function to change the ratio of the data item and the boundary smoothing term in the energy function, so that the image segmentation effect is optimal. The segmentation results of different experimental show that the improved paper method has better segmentation precision and better segmentation of texture details for complex images.
Dynamic path planning based on variable step size rolling window derivation and obstacle prediction

Aiming at the problem of dynamic path planning for UAVs in complex environment, a path planning algorithm combining dynamic obstacle trajectory estimation and variable step rolling window deduction is proposed. When the dynamic obstacle distance is close to the UAV, the Kalman filter is used to estimate the dynamic obstacle trajectory. Then the window deduction is performed to judge the track area of the deduction window, so that the algorithm has certain "forward-looking". In turn, the window step size, the flight speed of the drone and the flight direction are changed, and the avoidance of the dynamic obstacle is completed, and the final goal is reached by the rolling plan. The experimental analysis shows that the algorithm can complete the dynamic path planning of UAVs in complex environment in real time.
CU placement over a reconfigurable wireless fronthaul in 5G networks with functional splits

Mobile network operators are currently facing a tremendous increase in the level of data traffic. Although cell size reduction is one of the most common ways used to accommodate such traffic demand, densely deployed small cells also dramatically increase the level of intercell interference. By centralizing baseband signal processing at powerful computing infrastructures, called centralized unit (CU) pools, cloud radio access network (C‐RAN) enables advanced coordination algorithms to be employed in dense small cell networks. In C‐RAN, due to stringent bandwidth and latency requirements at the fronthaul links, the optical fiber, thanks to its bandwidth and latency characteristics, continues to be the most prevalent fronthaul medium option. Nevertheless, the optical fiber is one of the fronthaul options, while C‐RAN (physical layer radio frequency [PHY‐RF] split) is one of the functional splits that can be defined each coming with different fronthaul requirements.
Interactive Curation of Datasets for Training and Refining Generative Models

We present a novel interactive learning‐based method for curating datasets using user‐defined criteria for training and refining Generative Adversarial Networks. We employ a novel batch‐mode active learning strategy to progressively select small batches of candidate exemplars for which the user is asked to indicate whether they match the, possibly subjective, selection criteria. After each batch, a classifier that models the user's intent is refined and subsequently used to select the next batch of candidates. After the selection process ends, the final classifier, trained with limited but adaptively selected training data, is used to sift through the large collection of input exemplars to extract a sufficiently large subset for training or refining the generative model that matches the user's selection criteria. A key distinguishing feature of our system is that we do not assume that the user can always make a firm binary decision (i.e., “meets” or “does not meet” the selection criteria) for each candidate exemplar, and we allow the user to label an exemplar as “undecided”. We rely on a non‐binary query‐by‐committee strategy to distinguish between the user's uncertainty and the trained classifier's uncertainty, and develop a novel disagreement distance metric to encourage a diverse candidate set. In addition, a number of optimization strategies are employed to achieve an interactive experience. We demonstrate our interactive curation system on several applications related to training or refining generative models: training a Generative Adversarial Network that meets a user‐defined criteria, adjusting the output distribution of an existing generative model, and removing unwanted samples from a generative model.
Corporal rotation for correction of isolated congenital dorsal curvature of the penis without shortening

IntroductionSignificant degrees of isolated (without epispadias) congenital dorsal curvature of the penis may interfere with sexual intercourse. Rotation of the corpora cavernosa may correct this condition with neither shortening nor erectile dysfunction. AimTo investigate the outcome of correction of isolated congenital dorsal penile curvature by corporal rotation. MethodsThe procedure was performed in eight patients with dorsal deviation of 70–90°. Through a ventral median raphe incision, the corpus spongiosum was mobilized from the corpora cavernosa by sharp dissection. Two parallel incisions were cut, one on the ventral aspect of each corpus cavernosum on each side of the midline. The incisions were approximated, achieving corporeal rotation and thereby correcting curvature. Main outcome measuresCorrection of curvature, penile length, and sexual function. ResultsCorrection of curvature was achieved in all patients, with no shortening, asymmetry, or erectile dysfunction, with the exception of one patient who had mild residual curvature and mild girth asymmetry. ConclusionManagement of dorsal congenital penile curvature by corporal rotation offers full correction with no compromise in length or deterioration of erectile function.