Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging application classes to save power in highly-utilized data centers

Data center energy consumption has become an increasingly significant contributor both to greenhouse emissions and costs. To increase utilization of individual hosts and improve efficiency, most modern data centers co-locate workloads belonging to different application classes, some being latency-sensitive (LS) and others best-effort (BE) which are more tolerant to performance variation. It is therefore necessary to design mechanisms that reduce power consumption even in the resulting high-utilization environment, while preserving LS task performance. Moreover, the abundance of different workloads and the security implications of public cloud make mechanisms that rely on extensive knowledge of workload characteristics or on application-exported metrics challenging to deploy. We present PACT, Per Application Class Turbo Controller, a system that leverages two novel mechanisms to reduce power consumption even in highly-utilized data centers. By treating applications like opaque boxes that do not need to provide application-specific performance signals, the first mechanism, Turbo Control, reduces power consumption by decreasing the operating frequency and throttling only BE tasks, without affecting performance-sensitive LS tasks. We identify the shortcomings of Turbo Control and increase its effectiveness by introducing CPUJailing, a mechanism that allocates different sets of cores to LS and BE applications. We deploy PACT (Turbo Control + CPUJailing) in production at Google's data centers and demonstrate that it provides workload-agnostic power savings of 9% on average together with a 4% performance improvement for LS tasks across thousands of workloads and nodes.
Towards time-efficient ghost imaging

Reducing the time necessary to acquire information is highly desirable in almost every context. Ghost imaging is no exception, which is very time consuming due to its scanning nature and low light levels innate to quantum experiments. This work aimed to reduce the time required to reconstruct the image whilst maintaining quality. In doing so, we followed two complementary approaches: one varying the experimental parameters, and another implementing computational processing. We defined a performance measure based on the image reconstruction time and its resemblance to the original object, and determined that the use of image processing and recognition algorithms offers major improvements in temporal efficiency. Importantly, if the main purpose of imaging is solely object recognition, low resolution mask patterns give better results, whereas higher resolution patterns yield better resolved images, at the expense of time. We believe this work will pique interest in the ghost and single-pixel imaging communities.
Concury: a fast and light-weight software cloud load balancer

A load balancer (LB) is a vital network function for cloud services to balance the load amongst resources. Stateful software LBs that run on commodity servers provide flexibility, cost-efficiency, and packet consistency. However, current designs have two main limitations: 1) states are stored as digests, which may cause packet inconsistency due to digest collisions; 2) the data plane needs to update for every new connection, and frequent updates hurt throughput and packet consistency. In this work, we present a new software stateful LB called Concury, which is the first solution to solve these problems. The key innovation of Concury is a new method to maintain large network states with frequent connection arrivals, which is succinct in memory cost, consistent under network changes, and incurs low update cost. The evaluation results show that the Concury algorithm provides 4x throughput and consumes less memory compared to other LB algorithms, while providing weighted load balancing and false-hit freedom, for both real and synthetic data center traffic. We implement Concury and evaluate it in two real networks. It achieves 67.2 Gbps single-thread throughput on a cheap desktop computer in 100GbE.
Moesin orchestrates the reorganisation of the actin cortex and shape changes during mitotic progression in an epithelium

Animal cells endure dramatic actin-dependent changes in shape as they progress through mitosis – they round up at mitotic entry, elongate at anaphase and split into two at cytokinesis. In this thesis I explore the role of Moesin, an actin-membrane crosslinker and the sole ERM protein expressed in Drosophila, in orchestrating rearrangements of the actin cortex and morphological changes in epithelial cells undergoing mitosis. To perform my studies I used the fly notum and sensory organ precursor (SOP) cells therein as a model system. In this thesis I show that Moesin is required for the stabilisation of the actomyosin cortex at metaphase. This mechanism is dependent upon phosphorylation of Moesin by the Slik kinase, which activates the ERM protein. Reduced levels of Moesin or Slik lead to myosin-II-driven cortical instabilities. Cortical stabilisation in mitotic SOP cells ensures the efficient accumulation of fate determinants at the plasma membrane. At mitotic exit, a pool of active, phosphorylated Moesin is lost from the cell poles, thereby triggering polar relaxation and initiating anaphase cell elongation. These two events precede furrow formation, are independent of centrosome or astral microtubules-derived signals, and are induced by proximity of the segregating chromosomes to the cell poles. I show that a pool of kinetochore-localised PP1-87B phosphatase and its regulatory subunit Sds22 inactivate cortical Moesin and elicit the dismantling of the actomyosin cortex at mid-anaphase. Cells with reduced amounts of PP1-87B or Sds22 fail to clear Moesin and actin from the anaphase poles. Importantly, these defects in polar relaxation are mimicked by the expression of a constitutively active form of Moesin in fly tissues. Finally, I demonstrate that delocalisation of PP1/Sds22 from the kinetochores via KNL1 depletion abolishes polar blebbing at anaphase and impairs cell elongation. My work shows how the dynamic regulation of Moesin activation and localisation controls shape changes in cells undergoing mitosis. Moreover, it sheds light on a novel mechanism of polar relaxation at anaphase, in which a kinetochore-derived signal instructs the cell cortex to become polarised, thereby initiating cytokinesis.
Establishment of detecting methods of Th1/Th2 cytokine gene

Objective: TO establish methods for sensitive, rapid, economical detection of Th1/Th2 Cytokine gene expression. Method: sixpairs of Primers specific for the IL-2, IL-4, IL-10, IL-13, IFNr and B-actin were designed. A Sensitive and rapid method for detection of Th1/Th2cytokine genes by RT-RCR was established. Meanwhile another method: the RNA hybridization with sir cDNA probes were established. Results:The RT-PCR reaction and RNA hybridization was used to test five kinds of Cytokine mRNAs expressed 20 tumor cell lines on mRNAs were expressed in eleven Cell lines;The mRNAs in eight cell lines. Conclusion:The preliminary results suggested that RT-PCR and RNA hybridizationnight be used to test Th1/Th2 cytokine gene expression.
Diffusible Tumor Necrosis Factor-Alpha (TNF-α) Promotes Noise-Induced Parvalbumin-Positive (PV+) Neuron Loss and Auditory Processing Impairments

Neuroinflammation has been implicated in noise-induced auditory processing disorder and tinnitus. Certain non-auditory neurological disorders can also increase the levels of proinflammatory cytokines in the brain. To investigate the impact of increased brain proinflammatory cytokine levels on the central auditory pathway, we infused recombinant TNF-α into the right lateral cerebral ventricle, and examined auditory processing and cytoarchitecture of the auditory cortex. Microglial deramification was observed in the auditory cortex of mice that had received both TNF-α infusion and exposure to an 86-dB noise, but not in mice that had received either TNF-α infusion or noise exposure alone. In addition, we observed reduced cortical PV+ neuron density and impaired performances in gap detection and prepulse inhibition (PPI) only in mice that received both TNF-α infusion and the noise exposure. These results suggest that disease-related increase in brain proinflammatory cytokine release could be a risk factor for noise-induced auditory processing disorder and tinnitus.
Neuroticism and Fear of COVID-19. The Interplay Between Boredom, Fantasy Engagement, and Perceived Control Over Time

The Italian government adopted measures to prevent the spread of coronavirus 2019 (COVID-19) infection from March 9, 2020, to May 4, 2020 and imposed a phase of social distancing and self-isolation to all adult citizens. Although justified and necessary, psychologists question the impact of this process of COVID-19 isolation on the mental health of the population. Hence, this paper investigated the relationship between neuroticism, boredom, fantasy engagement, perceived control over time, and the fear of COVID-19. Specifically, we performed a cross-sectional study aimed at testing an integrative moderated mediation model. Our model assigned the boredom to the mediation role and both the fantasy engagement and perceived control of time to the role of moderators in the relationship between neuroticism and the fear of COVID-19. A sample of 301 subjects, mainly women (68.8%), aged between 18 and 57 years (Mage = 22.12 years; SD = 6.29), participated in a survey conducted in the 1st-week lockdown phase 2 in Italy from May 7 to 18, 2020. Results suggested that neuroticism is crucial in coping with the COVID-19 pandemic, in line with literature showing high neurotic people having greater emotional reactivity and scarce resources to manage stress. We also found that people with high neuroticism tend to feel bored, and the relationship between neuroticism and boredom seems enhanced if one is involved in negative fantasies. Therefore, this result could also explain the positive effect between boredom and fear of COVID-19 we found in the current study. However, our data show that perceived control over time moderates the association between boredom and fear toward COVID-19. Having a high perceived control over time allows people to reduce boredom’s effect on fear of COVID-19. In conclusion, we retain that psychological treatment programs could improve the individuals’ perceived control over time to modulate anxiety toward the fear of COVID-19 and promote psychological well-being.
Fast BF-ICrA Method for the Evaluation of MO-ACO Algorithm for WSN Layout

In this paper, we present a fast Belief Function based Inter-Criteria Analysis (BF-ICrA) method based on the canonical decomposition of basic belief assignments defined on a dichotomous frame of discernment. This new method is then applied for evaluating the Multiple-Objective Ant Colony Optimization (MO-ACO) algorithm for Wireless Sensor Networks (WSN) deployment.
Potentials of digital approaches in a tourism industry with changing customer needs – a quantitative study

In recent years, the tourism industry has been undergoing a period of transformation, not least due to innovative and disruptive business models entering the market. Companies are increasingly focusing on customer needs and try to meet them with digital approaches. The early recognition of rapidly changing needs and their reactions is challenging. This paper examines four factors influencing the potentials of the changing customer needs and ongoing digitization in the tourism industry. A hypothesis model developed in previous qualitative research is examined in this paper with a quantitative approach using structural equation modeling. 157 responses from the target group were analyzed to test the factors digital marketing, data mining, digital services and online travel communities. The results of this paper show that digital marketing and data mining have a positive as well as highly significant influence on the potentials of digital approaches in a tourism industry with changing customer needs.
An Iterative Descent Method for Predicting The Compressive Cement Strength Estimated Parameters

—In this paper, we propose an iterative descent method to predict compressive cement strength estimated parameters for lime and cement as coating substances. We ﬁrst propose a formal description of the problem by using a mathematical model that is based upon a series of equations. The aforementioned equations are related to both the ratio of quantity of water to quantity of coating substance and the ratio of quantity of straw to quantity of coating substance. Second, we propose to solve the model by applying a gradient descent method. It is applied for reaching results closest to the data gathered from a real experimental studies conducted on the coating of ﬂax straw before incorporating them into a cement matrix. The experimental part shows that the proposed model is capable to predict The the estimation of the parameters necessary for such study.
Age-related Spike Timing Dependent Plasticity of Brain-inspired Model of Visual Information Processing with Reinforcement Learning

The paper summarizes our efforts to develop a spike timing neural network model of dynamic visual information processing and decision making inspired by the available knowledge about how the human brain performs this complicated task. It consists of multiple layers with functionality corresponding to the main visual information processing structures starting from the early level of the visual system up to the areas responsible for decision making based on accumulated sensory evidence as well as the basal ganglia modulation due to the feedback from the environment. In the present work, we investigated age-related changes in the spike timing dependent plastic synapses of the model as a result of reinforcement learning.
Increasing the Reusability of IoT-aware Business Processes

The Internet of Things (IoT) is based on connected devices which are often heterogeneous in terms of supported communication protocols, interfaces and message formats. IoT-aware business processes, which are executed by process engines, are often bound to specific device types. This decreases their reusability when they are ought to be deployed in multiple IoT scenarios where the ability of supporting different device types is an important requirement. In this paper, we introduce a novel approach on how to overcome the heterogeneity of IoT devices, thus increasing the reusability of IoT-aware business processes. The contribution of this work to information systems research is twofold: First, we present a device abstraction model as the basis to define business process tasks across heterogeneous device types without the need of dealing with their technical implementations. Secondly, we propose a system architecture which supports the modeling, deployment, execution and reuse of IoT-aware business processes.
An incremental malware detection model for meta-feature API and system call sequence

In this technical world, the detection of malware variants is getting cumbersome day by day. Newer variants of malware make it even tougher to detect them. The enormous amount of diversified malware enforced us to stumble on new techniques like machine learning. In this work, we propose an incremental malware detection model for meta-feature API and system call sequence. We represent the host behaviour using a sequence of API calls and system calls. For the creation of sequential system calls, we use NITRSCT (NITR System call Tracer) and for sequential API calls, we generate a list of anomaly scores for each API call sequence using Numenta Hierarchical Temporal Memory (N-HTM). We have converted the API call sequence into six meta-features that narrates its influence. We do the feature selection using a correlation matrix with a heatmap to select the best meta-features. An incremental malware detection model is proposed to decide the label of the binary executable under study. We classify malware samples into their respective types and demonstrated via a case study that, our proposed model can reduce the effort required in STS-Tool (Socio-Technical Security Tool) approach and Abuse case. Theoretical analysis and real-life experiments show that our model is efficient and achieves 95.2% accuracy. The detection speed of our proposed model is 0. 03s. We resolve the issue of limited precision and recall while detecting malware. User’s requirement is also met by fixing the trade-off between accuracy and speed.
Finding the right cloud configuration for analytics clusters

Finding good cloud configurations for deploying a single distributed system is already a challenging task, and it becomes substantially harder when a data analytics cluster is formed by multiple distributed systems since the search space becomes exponentially larger. In particular, recent proposals for single system deployments rely on benchmarking runs that become prohibitively expensive as we shift to joint optimization of multiple systems, as users have to wait until the end of a long optimization run to start the production run of their job. We propose Vanir, an optimization framework designed to operate in an ecosystem of multiple distributed systems forming an analytics cluster. To deal with this large search space, Vanir takes the approach of quickly finding a good enough configuration and then attempts to further optimize the configuration during production runs. This is achieved by combining a series of techniques in a novel way, namely a metrics-based optimizer for the benchmarking runs, and a Mondrian forest-based performance model and transfer learning during production runs. Our results show that Vanir can find deployments that perform comparably to the ones found by state-of-the-art single-system cloud configuration optimizers while spending 2X fewer benchmarking runs. This leads to an overall search cost that is 1.3--24X lower compared to the state-of-the-art. Additionally, when transfer learning can be used, Vanir can minimize the benchmarking runs even further, and use online optimization to achieve a performance comparable to the deployments found by today's single-system frameworks.
Bypassing the load balancer without regrets

Load balancers are a ubiquitous component of cloud deployments and the cornerstone of workload elasticity. Load balancers can significantly affect the end-to-end application latency with their load balancing decisions, and constitute a significant portion of cloud tenant expenses. We propose CRAB, an alternative L4 load balancing scheme that eliminates latency overheads and scalability bottlenecks while simultaneously enabling the deployment of complex, stateful load balancing policies. A CRAB load balancer only participates in the TCP connection establishment phase and stays off the connection's datapath. Thus, load balancer provisioning depends on the rate of new connections rather than the actual connection bandwidth. CRAB depends on a new TCP option that enables connection redirection. We provide different implementations for a CRAB load balancer on different technologies, e.g., P4, DPDK, and eBPF, showing that a CRAB load balancer does not require many resources to perform well. We introduce the connection redirection option to the Linux kernel with minor modifications, so that it that can be shipped with the VM images offered by the cloud providers. We show how the same functionality can be achieved with a vanilla Linux kernel using a Netfilter module, while we discuss how CRAB can work while clients and servers remain completely agnostic, based on functionality added on the host. Our evaluation shows that CRAB pushes the IO bottleneck from the load balancer to the servers in cases where vanilla L4 load balancing does not scale and provides end-to-end latencies that are close to direct communication while retaining all the scheduling benefits of stateful L4 load balancing.
Parallel implementation of a PIC simulation algorithm using OpenMP

Particle-in-cell (PIC) simulations are focusing on the individual trajectories of a very large number of particles in self-consistent and external electric and magnetic fields; they are widely used in the study of plasma jets, for example. The main disadvantage of PIC simulations is the large simulation runtime, which often requires a parallel implementation of the algorithm. The current paper focuses on a PIC1d3v simulation algorithm [1] [2] and describes the successful implementation of a parallel version of it on a multicore architecture, using OpenMP, with very promising experimental and theoretical results.
A Survey on Heterogeneous One-class Collaborative Filtering

Recommender systems play an important role in providing personalized services for users in the context of information overload. Generally, users’ feedback toward items often contain the most significant information reflecting their preferences, which enables accurate personalized recommendation. In real applications, users’ feedback are usually heterogeneous (rather than homogeneous) such as purchases and examinations in e-commerce, which reflects users’ preferences in different degrees. Effective modeling of such heterogeneous one-class feedback is challenging compared with that of homogeneous feedback of ratings. As a response, heterogeneous one-class collaborative filtering (HOCCF) is proposed, which often converts the heterogeneous feedback into two parts (i.e., target feedback and auxiliary feedback), aiming to care more about the target feedback (e.g., purchases) with the assistance of the auxiliary feedback (e.g., examinations). In this survey, we provide an overview of the representative HOCCF methods from the perspective of factorization-based methods, transfer learning-based methods, and deep learning-based methods. First, we review the factorization-based methods according to different strategies. Second, we describe the transfer learning-based methods with different knowledge sharing manners. Third, we discuss the deep learning-based methods according to the neural architectures. Moreover, we include some important example applications, describe the empirical studies, and discuss some promising future directions.
The general universal model of blockchain technology based on an analysis of some implementations

—First imlementation of blockchain technology was appeared in 2008, and 12 years later more than 2000 different implementations of it have appeared. After deep analysis we found that approaches for development blockchain technologies is fragmented, there are no common system of concepts and general model of technology. In this article we want to propose the general universal model and system of concepts for the blockchain technology irrespective of differenced of some implementations. Our approach is based on a technical analysis of the popular blockchains. The results of this work can be used by architects of new blockchains implementstions, by researchers to achieve theirs goals and also in educational process.