Miracle-dz/UniFAR-FTUs huggingface dataset:
MIMIQ: Masking IPs with Migration in QUIC

The emerging QUIC transport protocol offers new opportunities to protect user privacy. We present MIMIQ, a privacy-enhancing system that leverages QUIC to protect user identity and thwart trafﬁc-analysis attacks. MIMIQ leverages QUIC’s connection migration capability to change a client’s IP address frequently—even within individual connections— without disrupting ongoing transfers or changing the client’s physical location. MIMIQ is readily deployable, requiring no cooperation from networks other than the trusted network where it runs. The trusted network facilitates routing of return trafﬁc by running an address allocation server that assigns IP addresses to clients and forwarding rules to switches. By strategically choosing migration times, MIMIQ can defeat certain trafﬁc-analysis attacks while incurring low performance overhead.
Cost-effective Hardware Accelerator Recommendation for Edge Computing

Hardware accelerator devices have emerged as an alternative to traditional CPUs since they not only help perform computations faster but also consume much less energy than a traditional CPU thereby helping to lower both capex (i.e., lower procurement costs) and opex (i.e., lesser energy usage). However, since different accelerator technologies can illustrate different traits for different application types that run at the edge, there is a critical need for effective mechanisms that help developers select the right technology (or a mix of) to use in their context, which is currently lacking. To address this critical need, we propose a recommender system to help users rapidly and cost-effectively select the right hardware accelerator technology for a given compute intensive task. Our framework comprises the following workflow. First, we collect realistic execution traces of computations on real, single hardware accelertor devices. Second, we utilize these traces to deduce the achievable latencies and amortized costs of device deployments at scale, which serves as the guidance in selecting the right hardware.
Sharing and Caring of Data at the Edge

Edge computing is an emerging computing paradigm where data is generated and processed in the field using distributed computing devices. Many applications such as real-time video processing, augmented/virtual reality gaming, environment sensing, benefit from such decentralized, close-to-user deployments where low-latency, real-time results are expected. As with any distributed application, one of the key challenges in the development of collaborative applications is how to efficiently share data and state among multiple edge clients. The dynamic and heterogeneous environment together with diverse application’s requirements make data sharing at the edge a challenging problem. Although there have been prior efforts, a systematic understanding of the area is missing. In this paper, we conduct a methodological study of different edge applications, their data sharing needs, and designs of state-of-the-art systems. In the process, we identify design options, under-explored opportunities, and associated challenges. We then present Griffin, our edge data sharing service, and seek feedback on its design.
Toward Orchestration of Complex Networking Experiments

Experimentation is an essential tool for developing networked and distributed systems. However, it is inherently complex due to the concurrent, asynchronous, heterogeneous, and prototype-based systems that must be integrated into representative scenarios to conduct valid evaluations. This paper offers a retrospective on the development and use of MAGI, an orchestration tool, that translates an experiment specification into an execution on an emulation-based testbed with high-level directives for message passing, remote process execution, and failure tracking, for conducting large and complex experiments. The MAGI tool has been used for more than seven years in a variety of experiments, including undergraduate education, anonymous communication, cyber-physical systems, and attacker-defender games on the DETER testbed. We hope the insights and takeaways learned from using our tool will aid in developing the next-generation experiment management tools.
uTree

Tail latency is a critical design issue in recent storage systems. B+-tree, as a fundamental building block in storage systems, incurs high tail latency, especially when placed in persistent memory (PM). Our empirical study specifies two factors that lead to such latency spikes: (i) the internal structural refinement operations (i.e., split, merge, and balance), and (ii) the interference between concurrent operations. The problem is even worse when high concurrency meets with the low write bandwidth of persistent memory. In this paper, we propose a B+-tree variant named μTree. It incorporates a shadow list-based layer to the leaf nodes of a B+-tree to gain benefits from both list and tree data structures. The list layer in PM is exempt from the structural refinement operations since list nodes in the list layer own separate PM spaces, which are organized in an element-based way. Meanwhile, μTree still gains the locality benefit from the tree-based nodes. To alleviate the interference overhead, μTree coordinates the concurrency control between the tree and list layer, which moves the slow PM accesses out of the critical path. We compare μTree to state-of-the-art designs of PM-aware B+-tree indices under both YCSB workload and real-world applications. μTree achieves a 99th percentile latency that is one order of magnitude lower and 2.8 - 4.7 times higher throughput.
APTGen: An Approach towards Generating Practical Dataset Labelled with Targeted Attack Sequences

In incident response for targeted cyber attacks, the responders investigate the sequence of attacks (attack sequence) that intruders have followed by analyzing the remaining logs. Their goal is to grasp and understand the whole picture of the incident. For accelerating incident response, it is important to develop technologies to automate the investigation of the attack sequences. However, we see lack of open dataset that contains logs and corresponding attack sequence information in order to evaluate these technologies. In this paper, we propose APTGen, an approach for generating targeted attack dataset. APTGen is top-down, that is, it ﬁrst generates artiﬁcial attack sequence from existing security reports based on the attack model deﬁned in MITRE’s ATT&CK. Then, in order to obtain logs from execution environments, it executes corresponding attack tools to realize the attack sequences. Thanks to the top-down approach, we can obtain the attack sequence information corresponding to the attack trace left in the logs. We generate 800 different attack sequences and logs based on reports of eight actual security incidents. We publish generated sequences and logs as a dataset for R&D of incident responses.
POLARIS

In this paper, we describe the Polaris distributed SQL query engine in Azure Synapse. It is the result of a multi-year project to re-architect the query processing framework in the SQL DW parallel data warehouse service, and addresses two main goals: (i) converge data warehousing and big data workloads, and (ii) separate compute and state for cloud-native execution. From a customer perspective, these goals translate into many useful features, including the ability to resize live workloads, deliver predictable performance at scale, and to efficiently handle both relational and unstructured data. Achieving these goals required many innovations, including a novel "cell" data abstraction, and flexible, fine-grained, task monitoring and scheduling capable of handling partial query restarts and PB-scale execution. Most importantly, while we develop a completely new scale-out framework, it is fully compatible with T-SQL and leverages decades of investment in the SQL Server single-node runtime and query optimizer. The scalability of the system is highlighted by a 1PB scale run of all 22 TPC-H queries; to our knowledge, this is the first reported run with scale larger than 100TB.
HTTPT: A Probe-Resistant Proxy

Recently, censors have been observed using increasingly sophisticated active probing attacks to reliably identify and block proxies. In this paper, we introduce HTTPT, a proxy designed to hide behind HTTPS servers to resist these active probing attacks. HTTPT leverages the ubiquity of the HTTPS protocol to effectively blend in with Internet trafﬁc, making it more difﬁcult for censors to block. We describe the challenges that HTTPT must overcome, and the beneﬁts it has over previous probe-resistant designs.
This paper is included in the Proceedings of the Sixteenth Symposium on Usable Privacy and Security.

Security awareness and education programmes are rolled out in more and more organisations. However, their e ﬀ ective-ness over time and, correspondingly, appropriate intervals to remind users’ awareness and knowledge are an open question. In an attempt to address this open question, we present a ﬁeld investigation in a German organisation from the public administration sector. With overall 409 employees, we evaluated (a) the e ﬀ ectiveness of their newly deployed security awareness and education programme in the phishing context over time and (b) the e ﬀ ectiveness of four di ﬀ erent reminder measures – administered after the initial e ﬀ ect had worn o ﬀ to a degree that no signiﬁcant improvement to before its deployment was detected anymore. We ﬁnd a signiﬁcantly improved performance of correctly identifying phishing and legitimate emails directly after and four months after the programme’s deployment. This was not the case anymore after six months, indicating that reminding users after half a year is recommended. The investigation of the reminder measures indicates that measures based on videos and interactive examples perform best, lasting for at least another six months.
EdgeBalance: Model-Based Load Balancing for Network Edge Data Planes

Edge data centers are an appealing place for telecommunication providers to offer in-network processing such as VPN services, security monitoring, and 5G. Placing these network services closer to users can reduce latency and core network bandwidth, but the deployment of network functions at the edge poses several important challenges. Edge data centers have limited resource capacity, yet network functions are resource intensive with strict performance requirements. Replicating services at the edge is needed to meet demand, but balancing the load across multiple servers can be challenging due to diverse service costs, server and flow heterogeneity, and dynamic workload conditions. In this paper, we design and implement a model-based load balancer EdgeBalance for edge network data planes. EdgeBalance predicts the CPU demand of incoming traffic and adaptively distributes flows to servers to keep them evenly balanced. We overcome several challenges specific to network processing at the edge to improve throughput and latency over static load balancing and monitoring-based approaches.
Turbo Tunnel, a good way to design censorship circumvention protocols

This paper advocates for the use of an interior session and reliability layer in censorship circumvention systems—some protocol that provides a reliable stream interface over a possibly unreliable or transient carrier protocol, with sequence numbers, acknowledgements, and retransmission of lost data. The inner session layer enables persistent end-to-end session state that is largely independent of, and survives disruptions in, the outer obfuscation layer by which it is transported. The idea—which I call Turbo Tunnel—is simple, but has many benefits. Decoupling an abstract session from the specific means of censorship circumvention provides more design flexibility, and in some cases may increase blocking resistance and performance. This work motivates the concept by exploring specific problems that a Turbo Tunnel design can solve, describes the essential components of such a design, and reflects on the experience of implementation in the obfs4, meek, and Snowflake circumvention systems, as well as a new DNS over HTTPS tunnel.
Toward an interdisciplinary integration between multi-agents systems and multi-robots systems: a case study

Abstract Multi-Robot System (MRS) is composed of a group of robots that work cooperatively. However, Multi-Agent System (MAS) is computational systems consisting of a group of agents that interact with each other to solve a problem. The central difference between MRS and MAS is that in the first case, the agent is a robot, and in the second, it is a software. Analyzing the scientific literature, it is possible to notice that few studies address the integration between MAS and MRS. In order to achieve the interdisciplinary integration, the theoretical background of these areas must be considered in this paper, so that the integration can be applied using a case study of decentralized MRS. The objective of this MRS is to track and surround a stationary target. Also, it has been implemented and validated in the robot simulator called Virtual Robot Experimentation Platform (V-REP). In the validation of the proposed MRS, a scenario with three robots and a stationary target were defined. In the tracking task, the robot can detect the target whose position is not known a priori. When the detection occurs, the V-REP informs the target position to the robot because the environment is discretized into a grid of rectangular cells. After that, all the robots are directed to the target, and the surround task is realized. In this task, a mathematical model with direct communication between the robots was used to keep the robots equidistant therefrom and from each other.
Secure Data Processing at Scale

Although the cloud is today a de-facto standard for scalable data processing, there are still many applications that cannot make use of the cloud due to data or computation privacy. Sensitive data, such as in the health domain; and computations, such as core-business AI pipelines, grew into valuable assets that made secure data processing a hot topic in industry and academia. On one hand, the existing data processing systems prioritize performance and, to a certain level, trade users’ privacy. On the other hand, privacy-preserving data processing systems sacriﬁce performance. In this PhD thesis, we envision a fully secure general-purpose data processing system for the cloud. Overall, we aim at devising: (i) algorithms that are adequate to work with very limited memory, such as the one exposed by trusted execution environments; (ii) scalable state management techniques; (iii) oblivious data-access algorithms; and (iv) privacy-preserving query optimizations techniques to speed up query execution.
(Artificial) Mind over Matter: Humans In and Humans Out in Matching

The matching task is at the heart of data integration, in charge of aligning elements of data sources. Historically, matching problems were considered semi automated tasks in which correspondences are generated by matching algorithms and subsequently validated by human expert(s). This research is devoted to the changing role of humans in matching, which is divided into two main approaches, namely Humans Out and Humans In . With the increase in amount and size of matching tasks, the role of humans as val-idators seems to diminish; thus Humans In questions the inherent need for humans in the matching loop. On the other hand, Humans Out focuses on overcoming human cognitive biases via algorithmic assistance. Above all, we observe that matching requires unconventional thinking demonstrated by advance machine learning methods to complement (and possibly take over) the role of humans in matching.
Complex Event Processing on Real-time Video Streams

Cameras are ubiquitous nowadays and video analytic systems have been widely used in surveillance, traffic control, business intelligence and autonomous driving. Some applications, e.g., detecting road congestion in traffic monitoring, require continuous and timely reporting of complex patterns. However, conventional complex event processing (CEP) systems fail to support video processing, while the existing video query languages offer limited support for expressing advanced CEP queries, such as iteration, and window. In this PhD research, we aim to develop systems and methods to alleviate these issues. In this paper, we first identify the need for an expressive CEP language which allows users to define queries over video streams, and receive fast, accurate results. To evaluate CEP queries on videos in real-time and with high accuracy, we explain how a streaming query engine can be designed to provide native support of machine learning (ML) models for fast and accurate inference on video streams. In addition, we describe a set of optimization problems that arise when ML models, with trade-offs in speed, accuracy, and cost, are part of a query plan. Finally, we describe how query plans on real-time videos can be optimized and deployed on edge devices with limited computational and network capabilities.
Replication at the speed of change

The IBM Db2 Analytics Accelerator (IDAA) is a state-of-the art hybrid database system that seamlessly extends the strong transactional capabilities of Db2 for z/OS with the very fast column-store processing in Db2 Warehouse. The Accelerator maintains a copy of the data from Db2 for z/OS in its backend database. Data can be synchronized at a single point in time with a granularity of a table, one or more of its partitions, or incrementally as rows changed using replication technology. IBM Change Data Capture (CDC) has been employed as replication technology since IDAA version 3. Version 7.5. 0 introduces a superior replication technology as a replacement for IDAA's use of CDC - Integrated Synchronization. In this paper, we present how Integrated Synchronization improves the performance by orders of magnitudes, paving the way for near real-time Hybrid Transactional and Analytical (HTAP) processing.
Extended Log-logistic Model Comparison of Adjuvant Chemotherapy Regimes for Breast Cancer in Iran

Breast cancer (BC) is the most frequent cancer among Iranian women, comprising 23.6% of all recorded cases of cancer (Cancer office of center for disease control and prevention, 2007; Sadjadi et al., 2009). Based on (Harirchi et al., 2004), BC in Iranian women occurs 10 years earlier compared to those living in developed countries. Although adjuvant chemotherapy treatments after surgery are among the options women with breast cancer have, the number of studies conducted in Iran in order to determine the appropriate adjuvant therapy is still scarce (Fazl-Ali Zadeh and Hatami, 2004; Bakhtyari and Hajyan, 2007; Kadivar and Bozorgmehr 2007). Moreover, the optimal type of adjuvant chemotherapy is still a matter of debate (Cocconi et al., 2004; Hayes, 2009). Whatever the case, however, adjuvant chemotherapy is believed to increase disease-free survival (DFS) and overall survival (OS) following surgery for BC patients (Early Breast Cancer
Management of sodium–glucose cotransporter 2 inhibitors during the perioperative period: A retrospective comparative study

Purpose Current guidelines recommend withholding sodium–glucose cotransporter 2 inhibitors perioperatively due to concerns of euglycaemic diabetic ketoacidosis. However, such guidelines are largely based on case reports and small case series, many extrapolated from non-surgical patients. The aim was to investigate whether withholding sodium–glucose cotransporter 2 inhibitors as per current perioperative guidelines was associated with a reduction in serious adverse events, including euglycaemic diabetic ketoacidosis. Methods Instances of perioperative management of sodium–glucose cotransporter 2 inhibitors, over a four-year period were classified into two categories: those where sodium–glucose cotransporter 2 inhibitors were withheld as per guidelines and those where sodium–glucose cotransporter 2 inhibitors were administered in the perioperative period. The primary outcome was ‘total major perioperative complications’: a composite of serious adverse events including euglycaemic diabetic ketoacidosis, diabetic ketoacidosis, acute kidney injury, urosepsis and death. Results Eighty-two instances in 64 patients were included. Withholding sodium–glucose cotransporter 2 inhibitors was associated with an increased incidence of total major perioperative complications and poorer glycaemic control postoperatively. Multivariable logistic regression analysis revealed that withholding sodium–glucose cotransporter 2 inhibitors perioperatively (OR = 13.15; 95% CI = 1.8–138.9) and preoperative urea (OR 1.85 (95% CI = 1.17–3.43) were independently associated with an increase in total major postoperative complications. Conclusion Withholding sodium–glucose cotransporter 2 inhibitors as per current guidelines was associated with an increase in postoperative complications and reduced glycaemic control.
Effects of Acute and Chronic Graft-versus-myelodysplastic Syndrome on Long-term Outcomes Following Allogeneic Hematopoietic Cell Transplantation

Purpose: Potent graft-versus-tumor (GVT) effects associated with graft-versus-host disease (GVHD) might be dependent on hematologic disease type and status. However, the data regarding the impact of GVHD on transplant outcomes for patients with myelodysplastic syndrome (MDS) are limited. Experimental Design: We retrospectively evaluated the impact of acute and chronic GVHD on transplant outcomes for a large cohort of adult patients with a low-risk (n = 1,193) and high-risk (n = 1,926) MDS treated by first allogeneic hematopoietic cell transplantation between 2001 and 2017. Results: The multivariate analysis, in which development of GVHD was treated as a time-dependent covariate, showed that acute and chronic GVHD at any grade or severity did not improve overall mortality, relapse, or nonrelapse mortality (NRM) in low-risk MDS. For patients with high-risk MDS, development of limited chronic GVHD was significantly associated with lower overall mortality [HR, 0.66; 95% confidence interval (CI), 0.50–0.86; P = 0.002]. This is probably due to that the reduced risk of relapse with grade III–IV acute GVHD (HR, 0.41; 95% CI, 0.25–0.65; P = 0.0002), or limited (HR, 0.57; 95% CI, 0.39–0.83; P = 0.003) or extensive (HR, 0.56; 95% CI, 0.41–0.77; P = 0.0004) chronic GVHD was offset by increased NRM with grade III–IV acute GVHD or extensive chronic GVHD in high-risk MDS. Conclusions: These data demonstrated a survival benefit of the graft-versus-MDS effect is present only in high-risk MDS patients with limited chronic GVHD. See related commentary by Eckel and Deeg, p. 6404
Isolation and Characterization of Lactic Acid Bacteria Involved In the Fermentation of Millet and Sorgum Sold In Nkwo-Achara Market, Abia State

The isolation and characterization of lactic acid bacteria responsible for the fermentation of kunuzaki and burukutu were studied in order to access their use as starter culture. Representatives of the lactic acid bacteria were isolated at 12hours interval during the course of fermentation characterized and identified using morphological, physiological and biochemical examination. Changes in pH, titratable acidity, LAB counts were also investigated during the fermentation of the two cereals at different fermentation time (12hrs and 24hrs) . The isolates obtained were accessed to have some level of starter culture. Based on the comparative performance of single isolates to a substrate and pH reduction. Isolates identified are lactobacillus plantarum, lactobacillus cellobiosus, lactobacillus pentosus, leuconostoc mesenteriods, and pediococcus pentosaceus. Results indicates a predominance of lactobacilli among isolates. Lactic acid bacterial counts in the kunu-zaki and burukutu were isolated at levels of 10, 10, cfu/ml. A very high reduction in pH was associated with an increase in titratable acidity. Analysis of variance show significance difference between pH of all the isolates in the two substrates used at P < 0.05% of freedom.