Miracle-dz/UniFAR-FTUs huggingface dataset:
AutoTuneTMP: Auto-Tuning in C++ With Runtime Template Metaprogramming

Careful tuning of code is crucial to obtain near-optimal runtime performance on the hardware at hand. How-ever, current hardware platforms pose plenty of challenges: multiple levels of cache, many cores and increasingly wider vector units render manual tuning time-consuming and cumbersome. Thus, algorithms have to be able to automatically tune themselves to the underlying hardware platform to maximize performance and to ensure performance portability. In this work, we present AutoTuneTMP, a novel C++-based auto-tuning library. Its unique strength is to combine template metaprogramming and just-in-time (JIT) compilation so that templates can be instantiated at runtime. We use this runtime metaprogramming approach to provide an extensible set of parameterized template-based optimizations and data structures for writing tunable kernels. Together with convenience functionality for parameter tuning, this lightweight approach can be used to implement algorithms with near-optimal and portable performance. We demonstrate the applicability, usefulness and performance of AutoTuneTMP at the example of matrix multiplication. It is well-suited as a demonstrator, being a well-known compute kernel and exhibiting a rather large set of (nine) tunable parameters to be optimized. With simple search algorithms, AutoTuneTMP is able to automatically achieve up to 90% of the peak performance on a Xeon Silver 4116 processor and up to 34% on a Xeon Phi 7210. Starting from an already parallelized and vectorized baseline, we obtained a speedup of up to 3.1x on the Xeon Silver platform and up to 6.7x on the Xeon Phi platform.
Optimal Information Centric Caching in 5G Device-to-Device Communications

Device-to-Device (D2D) communications are a prominent feature of 5G systems, introduced to provide a native support to distributed services in mobile environments. D2D technologies enable straight interactions between mobile terminals without a compulsory involvement of base stations. In this manuscript, we study and propose an optimized caching strategy to content distribution on top of D2D technology, based on Information Centric Networking (ICN) principles. The rationale is that ICN architectures can provide seamless support to mobile services and decouple contents from node identifiers, thus providing a promising match with D2D requirements. To this end, a novel fluid-based model in proposed hereby that catches the interplay between ICN functionalities, D2D requirements, and 5G specifications. Then, based on this model, an optimal content replication problem is formulated, encompassing caching overhead and system load. Additionally, this problem is thoroughly analyzed to prove that it has an optimal solution with time threshold form. A practical algorithm <inline-formula><tex-math notation="LaTeX">$\varsigma ^*$</tex-math><alternatives> <inline-graphic xlink:href="grieco-ieq1-2794970.gif"/></alternatives></inline-formula>-OCP is further proposed in order to implement the optimal caching control in realistic environments. Finally, a massive simulation campaign is carried out to test the proposed algorithm in comparison to state-of-the-art solutions.
Estimating Contact Forces and Moments for Walking Robots and Exoskeletons Using Complementary Energy Methods

When walking robots and exoskeletons make multiple independent contacts, the inverse dynamics problem requires additional knowledge about the contact forces and moments. To avoid measuring the contact forces and moments, many inverse dynamics controllers for walking robots optimize an objective such as minimizing torques or contact forces. In order to get a solution closer to the real solution, the underlying physical principles need to be included. This is achieved by relying on the minimization of complementary energy, which is a well known method in structural engineering. The proposed method relies on physical properties (stiffness) to obtain the additional knowledge to solve the contact forces and moments. In addition, it has the same form as the methods used in the literature. The validation on a bilateral lower-limb exoskeleton shows that the proposed method is able to predict the contact forces and moments sufficiently well, while being robust against modeling errors. The modeling dominating flexibilities suffices to achieve adequate results, making this method especially interesting for series elastic actuated robots.
Efficient Time-Slot Adjustment and Packet-Scheduling Algorithm for Full-Duplex Multi-Hop Relay-Assisted mmWave Networks

Millimeter wave (mmWave) technology is one of the innovations with the most potential for the fifth generation (5G) of wireless communication systems. With wider bandwidth and the ability to provide high-quality services, 5G systems are expected to significantly improve network performance. However, mmWave communications are vulnerable to blockage problems. Fortunately, efficient relaying and scheduling scheme can both solve the blockage problem and improve performance for the concurrent transmissions. With this paper, we aim to enhance the efficiency of concurrent transmissions for the mmWave-based networks. For the purpose of serving the transmission requests within minimal time, the full-duplex relay and adaptive transmission rates are first considered for designing the scheduling algorithm. To further enhance the scheduling flexibility, a larger scheduling space is obtained by using smaller scheduling unit. To achieve this, a smaller time slot is properly redefined so that the packet-by-packet transmissions (rather than the burst-by-burst fashion in the conventional scheme) can be carried out. Then, we propose an efficient time slot adjustment scheduling algorithm (named ETA) for multihop transmissions that incorporates full-duplex relaying, adaptive rates, and packet-by-packet transmissions. Using the simulation results, we verify the superior performance of the proposed ETA scheduling algorithm in terms of the time required to serve transmission requests.
Adaptive Streaming of HEVC Tiled Videos Using MPEG-DASH

Adaptive streaming technologies are used heavily to deliver video content over the Internet, but still face problems in networks with limited bandwidth, for videos with ultra-high definition (UHD) or greater, even when using the latest High-Efficiency Video Coding (HEVC) standard. In this paper, we propose a solution to these problems based on three things: a specific HEVC-compliant motion-constrained tiled encoding allowing tile-based decoding and rendering; a specific derivation of the ISO base media file format enabling a tile-based encapsulation and delivery; and their use in adaptive streaming of tiled video content using MPEG dynamic adaptive streaming over HTTP requiring only a single standard HEVC decoder on the client side. We present an evaluation of constrained and unconstrained HEVC tiling compression, and also of the associated packaging and streaming overheads. The results of this evaluation indicate that our approach incurs a complete overhead of at most 7.3% for UHD content, at 10 Mbps or above, when split in 25 tiles, and only 3.5% for nine tiles, when compared with the streaming of the non-tiled video. Our approach also comes with a small impact on client-side players but provides the interesting benefit of being able to deliver UHD videos over bandwidth-constrained networks adaptively, based on its tiled content.
Efficient Solving of Scan Primitive on Multi-GPU Systems

GPUs fulfill high computation demands, but it is necessary to develop code carefully, selecting algorithms well suited to the GPU architecture and applying different optimizations. This article presents a GPU-suitable algorithm and a tuning strategy for performing the scan primitive over large problem sizes in CUDA. This tuning strategy defines different performance premises to find the GPU execution parameters that maximize performance. Taking these premises into consideration, we easily develop the kernels using CUDA skeletons to ensure efficiency and portability. Based on this, we describe an optimal proposal analyzed over different multiple GPU environments, the first multiple-GPU batch scan proposal to the best of our knowledge. The resulting implementations outperform other well-known libraries in most cases, such as CUDPP, ModernGPU, Thrust, CUB and LightScan.
Improving I/O Performance Through Colocating Interrelated Input Data and Near-Optimal Load Balancing

Most distributed file systems assign new files to storage servers randomly. While working well in some situations, this does not help to optimize the input performance for most MapReduce computations' data access patterns. In this work, we consider an access pattern where input files are partitioned into groups of heterogeneous size. Each group is accessed by exactly one process. We design and implement a data placement strategy that places these file groups together on the same storage server. This colocation approach is combined with near-optimal storage load balancing. To do so, we use a classical scheduling approximation algorithm to solve the NP hard group assignment problem. We argue that local processing is not only beneficial because of reduced network traffic, but especially because it imposes an even resource schedule. Our experiments, based on the parallel processing of remote sensing images, reveal an enormous reduction of network traffic and up to 39 % faster input read times. Further, simulations show that our approximate assignments limit storage server imbalances to less than 5 % above the theoretical minimum, in contrast to more than 85 % with random assignment.
Background Subtraction Using Dominant Directional Pattern

Background subtraction technique is commonly used in foreground segmentation problem. Most of the existing color or intensity feature-based background subtraction methods suffer in non-stationary environments, such as presence of illumination variations. To address the issue, we introduce a new spatial feature descriptor that extracts the prominent directional information in a local neighborhood of a pixel. We use this local feature along with color information as the core component of a sample consensus-based model evolved from visual background extractor method. We also introduce an adaptive way to fuse color and local feature at each pixel to determine if the current observed features match to its corresponding background model while classification. The discriminative nature of our proposed local feature descriptor makes our model robust against the change of illumination. Extensive experiments on CDnet 2012 (changedetection.net) data set demonstrate that our background subtraction method outperforms many state-of-the-art methods.
Late Fusion of Local Indexing and Deep Feature Scores for Fast Image-to-Video Search on Large-Scale Databases

Low cost visual representation and fast query-by-example content search are two challenging objectives which should be supplied for web-scale visual retrieval task on moderate hardwares. In this paper, we introduce a fast yet robust method that ensures these two objectives by obtaining the state-of-the-art results for the image-to-video search scenario. For this purpose, we present critical improvements to the commonly used indexing and visual representation techniques by promoting faster, better and modest retrieval performance. Also, we boost the effectiveness of the method for visual distortions by exploiting the individual decision scores of local and global descriptors in the query time. By this way, local content descriptors effectively depict copy/duplicate scenes with large geometric deformations, while global descriptors are more practical for the near-duplicate and semantic search. Experiments are conducted on the large-scale Stanford I2V dataset. The experimental results show that the method is effective in terms of complexity and query processing time for large-scale visual retrieval scenarios, even if local and global representations are used together. Moreover, the proposed method is quite accurate and obtains state-of-the art performance based on the mAP score on the dataset. Lastly, we report additional mAP scores after updating the ground annotations obtained by the retrieval results of the proposed method which demonstrates the actual performance more clearly.
A Population Protocol for Uniform k-Partition Under Global Fairness

In this paper, we consider a uniform k-partition problem in a population protocol model. The uniform k-partition problem divides a population into k groups of the same size. For this problem, we give a symmetric protocol with designated initial states under global fairness. This protocol requires 3k-2 states; that is, the protocol is asymptotically space-optimal.
Feature Regularization and Deep Learning for Human Resource Recommendation

A novel recommender system is proposed in this paper. It has been implemented for human resource recommendation and achieved improvement on different evaluation metrics. The algorithm leverages both gradient boosting tree model and a convolutional network-based deep learning model for feature regularization and recommendation. The optimizations of activation function and pooling strategy in the proposed network model have been investigated for mitigating the problems of the gradient disappearance and the feature loss in pooling and for the improvement of recommendation quality. Human resource datasets are fetched by using a cloud-based distributed data collecting framework. Using the datasets, experiments on the proposed recommender system have been done and analyzed. Our proposed algorithm shows better recall rate and F1-score than some other recommender algorithms.
Collective Well-Being to Improve Population Health Outcomes: An Actionable Conceptual Model and Review of the Literature

Objectives: To propose collective well-being as a holistic measure of the overall “health” of a community. To define collective well-being as a group-level construct measured across 5 domains (vitality, opportunity, connectedness, contribution, and inspiration) and introduce an actionable model that demonstrates how community characteristics affect collective well-being. To review the literature describing each domain’s association with health outcomes and community characteristics’ associations with collective well-being. Methods: We came to consensus on topics describing each component of our conceptual model. Because “well-being” is not indexed in MEDLINE, we performed topic-specific database searches and examined bibliographies of papers retrieved. We excluded articles that were limited to narrow subtopics or studies within small subpopulations. Preference was given to quasi-experimental or randomized studies, systematic reviews, or meta-analyses. Consensus was reached on inclusion or exclusion of all articles. Results: Reviewed literature supported each of the proposed domains as important aspects of collective well-being and as determinants of individual or community health. Evidence suggests a broad range of community characteristics support collective well-being. Conclusions: The health and quality of life of a community may be improved by focusing efforts on community characteristics that support key aspects of well-being. Future work should develop a unified measure of collective well-being to evaluate the relative impact of specific efforts on the collective well-being of communities.
Towards a Bio-inspired ACO Approach for Building Collaborative Learning Teams

Collaborative learning is an educational method used by teachers to impact learning in a positive way and enhance the learners’ performances. It can occur within a group of two or more students. Therefore, the learners’ grouping is an essential task for establishing collaboration and cooperation among them to achieve a common goal. However, with the increasing number of students as well as their different settings (academic, cognitive, emotional, social, etc.), the students grouping task becomes harder and more complex to build a good collaborative learn- ing environment. Hence, several researchers proposed different approaches for obtaining approximate solutions to handle this diversity and take advantages from it. In this context, this paper presents a systematic literature review (SLR) to pinpoint the relevant research works that address the students grouping problem. We also we focus on the ACO bio-inspired approach to discuss its advantages and how students group formation can be initially constructed by ACO method. In accordance with this, we adapt and simulate an existing ACO algorithm with randomly generated data. The obtained results show an interesting starting point for further works in order to develop a new approach based on real educational data.
Polymer Nanocomposites based on in situ reduced graphene oxide for photovoltaic applications in innovative hybrid solar cells

Polymeric nanocomposites based on UV in situ reduced graphene oxide were developed in order to enhance the electrical conductivity of polymeric matrices for the realization of highly efficient solid-state hybrid solar cells based on organometal trihalide perovskite absorbers. Graphene is a 2-D single layer of sp2-bonded carbon atoms characterized by high specific surface area, Young's modulus, thermal stability, mobility of charge carriers and plus fascinating transport phenomena such as the quantum Hall effect. Among several developed methods to prepare graphene, including chemical vapor deposition (CVD) and mechanical exfoliation, the chemical reduction of graphene oxide (GO) is regarded as the most promising for future implementation in large-scale production. In this work, a GO prepared by a modified Hummers method was used and reduced by a green method based on UV treatment in inert atmosphere. Graphene oxide reduction was monitored evaluating the change of absorption peak by UV-visible spectroscopy and X-ray photoelectron spectroscopy (XPS), monitoring the decrease of the oxygen groups linked to carbon. Dilute nanocomposites suspensions were then prepared by optimizing the dispersion procedure of GO in the polymeric matrix as function of process parameters, i.e. time, temperature and composition. Nanocomposite films were also realized by spin coating on different substrates and characterized by several techniques. At last the film showing the better properties was implemented in a hybrid solar cell to evaluate the increase of electrical conductivity and power conversion efficiency.
Speaker Verification Using Adapted Bounded Gaussian Mixture Model

In this paper, we propose the application of bounded Gaussian mixture model (BGMM) to speaker verification. In the proposed approach, BGMM is employed for universal background model (UBM) and adapted speaker model. The proposed UBM is a large BGMM trained to represent speaker-independent distribution of features. In adapted speaker approach, hypothesized speaker model is derived by adapting the parameters of BGMM based UBM using speaker's training speech and maximum a posteriori (MAP). We have applied TIMIT and TSP speech corpora for the development of UBM and further testing of speaker verification by adapted speaker model. The proposed framework has demonstrated its effectiveness by improved speaker detection rate.
A Two-Stage Approach for Robust HEVC Coding and Streaming

The increased compression ratios achieved by the High Efficiency Video Coding (HEVC) standard lead to reduced robustness of coded streams, with increased susceptibility to network errors and consequent video quality degradation. This paper proposes a method based on a two-stage approach to improve the error robustness of HEVC streaming, by reducing temporal error propagation in the case of frame loss. The prediction mismatch that occurs at the decoder after frame loss is reduced through the following two stages. First, at the encoding stage, the reference pictures are dynamically selected based on constraining conditions and Lagrangian optimization, which distributes the use of reference pictures, by reducing the number of prediction units that depend on a single reference. Second, at the streaming stage, a motion vector (MV) prioritization algorithm, based on spatial dependencies, selects an optimal subset of MVs to be transmitted, redundantly, as side information to reduce mismatched MV predictions at the decoder. The simulation results show that the proposed method significantly reduces the effect of temporal error propagation. Compared with the reference HEVC, the proposed reference picture selection method is able to improve the video quality at low-packet-loss rates (e.g., 1%) using the same bitrate, achieving quality gains up to 2.3 dB for 10% of packet loss ratio. It is shown, for instance, that the redundant MVs are able to boost the performance achieving quality gains of 3 dB when compared with the reference HEVC, at the cost using 4% increase in total bitrate.
Spatial and temporal analysis of PCP protein dynamics during neural tube closure

Planar cell polarity (PCP) controls convergent extension and axis elongation in all vertebrates. Although asymmetric localization of PCP proteins is central to their function, we understand little about PCP protein localization during convergent extension. Here, we use quantitative live imaging to simultaneously monitor cell intercalation behaviors and PCP protein dynamics in the Xenopus laevis neural plate epithelium. We observed asymmetric enrichment of PCP proteins, but more interestingly, we observed tight correlation of PCP protein enrichment with actomyosin-driven contractile behavior of cell-cell junctions. Moreover, we found that the turnover rates of junctional PCP proteins also correlated with the contractile behavior of individual junctions. All these dynamic relationships were disrupted when PCP signaling was manipulated. Together, these results provide a dynamic and quantitative view of PCP protein localization during convergent extension and suggest a complex and intimate link between the dynamic localization of core PCP proteins, actomyosin assembly, and polarized junction shrinking during cell intercalation in the closing vertebrate neural tube.
Exploring the Use of Autoencoders for Botnets Traffic Representation

Botnets are a significant threat to cyber security. Compromised, a.k. a. malicious hosts in a network have, of late, been detected by machine learning from hand-crafted features directly sourced from different types of network logs. Our interest is in automating feature engineering while examining flow data from hosts labeled to be malicious or not. To automatically express full temporal character and dependencies of flow data requires time windowing and a very high dimensional set of features, in our case 30,000. To reduce dimensionality, we generate a lower dimensional embedding (64 dimensions) via autoencoding. This improves detection. We next increase the volume in the flows originating from hosts in our dataset known to be malicious or not by injecting noise we mix in from background traffic. The resulting lower metaphorical signal to noise ratio makes the presence of a bot even more challenging to detect so we resort to a filter encoder or an off-the-shelf denoising autoencoder. Both the filter encoding and denoising autoencoder improve upon detection compared to when hand-crafted features are used and are comparable in performance to the autoencoder.