Miracle-dz/UniFAR-FTUs huggingface dataset:
QUALITY OF THE INTEGRATED E-LEARNING SYSTEM – STUDENTS’ PERSPECTIVE

Implementation and use of e-learning in higher education has become a necessity in the 21st century. While first researches were focused more on the functional understanding how to use the technology and which ICT skills are needed in order to get better results from it, latest studies are focused more on the user satisfaction with tools such as the e-learning systems and how they can contribute to user motivation for learning. More precisely today’s e-learning research is concentrated on the quality of elearning systems. E-learning systems (also called course management systems or e-learning platforms) are considered as the most basic e-learning requirement and their ease of use encourages e-learning adoption. Within the E-learning Centre at the University Computing Centre University of Zagreb SRCE an e-learning platform named Merlin was established, available and free of charge to all university teachers and students. The e-learning platform Merlin is a complex system consisting of several tools and has grown to be virtual learning environment (VLE) for higher education teachers and students. The VLE Merlin is in operation for nine years now and hosts e-courses held at higher education institutions in the Republic of Croatia. In order to provide the ease of use and user friendly interface, the E-learning Centre conducted the survey on users’ attitudes towards usefulness of VLE Merlin, satisfaction with its design, functionality and user’ support that is provided by the E-learning Centre. Within this paper results of the students’ response are elaborated. The results showed that majority of students were satisfied with VLE Merlin, and find it easy to use. Students also consider VLE Merlin useful as it facilitates access to learning resources and communication with teachers and students. However, only half of the students, participating in the survey, expressed their satisfaction with the graphic design of the VLE Merlin. Some of the students left us comments regarding VLE Merlin design. We have considered students’ comments seriously and based upon them decided to make additional improvements to the VLE Merlin for the academic year 2016/2017.
Failure of the Problem-Oriented Medical Paradigm and a Person-Centered Alternative

Our problem-oriented approach to health care, though historically reasonable and undeniably impactful, is no longer well matched to the needs of an increasing number of patients and clinicians. This situation is due, in equal parts, to advances in medical science and technologies, the evolution of the health care system, and the changing health challenges faced by individuals and societies. The signs and symptoms of the failure of problem-oriented care include clinician demoralization and burnout; patient dissatisfaction and non-adherence; overdiagnosis and labeling; polypharmacy and iatrogenesis; unnecessary and unwanted end-of-life interventions; immoral and intolerable disparities in both health and health care; and inexorably rising health care costs. A new paradigm is needed, one that humanizes care while guiding the application of medical science to meet the unique needs and challenges of individual people. Shifting the focus of care from clinician-identified abnormalities to person-relevant goals would elevate the role of patients; individualize care planning; encourage prioritization, prevention, and end-of-life planning; and facilitate teamwork. Paradigm shifts are difficult, but the time has come for a reconceptualization of health and health care that can guide an overdue transformation of the health care system.
The ColonView (CV) Quick Test for Fecal Occult Blood Shows Significantly Higher Diagnostic Accuracy in Detecting Distal than Proximal Colorectal Cancer

Aim: The present study compared the accuracy of ColonView (CV) quick test in detecting proximal versus distal colorectal cancer (CRC). A traditional guaiac-based fecal occult blood test (gFOBT) (Hemoccult SENSA) was used as a reference. Patients and Methods: A cohort of 368 colonoscopy-referral patients were asked to collect 3 consecutive fecal samples, to be analyzed by both assays (CV, SENSA). Receiver operating characteristic (ROC) analysis was used to find the optimal cut-off values for both Hb and Hb/Hp of the CV test. Summary hierarchical ROC (HSROC) curves were used to visualize the pooled overall accuracy of visually analysed (VA) and automatically analyzed (AA) reading modes in proximal and distal CRC detection. Results: The overall specificity (Sp) of the AA reading mode for the proximal CRC and distal CRC endpoint was 73% and 76%, respectively. For proximal CRC, the two most sensitive AA tests showed 90% sensitivity (Se), while for distal CRC, the two most sensitive AA tests showed 100% Se. In the HSROC analysis, the AUC values were as follows: i) VA in proximal CRC: 0.765, ii) AA in proximal CRC: 0.878, iii) VA in distal CRC: 0.955 and iv) AA in distal CRC: 0.961. In roccomp analysis, AUC values were significantly different in: VA vs. AA in proximal CRC p=0.009; VA in proximal vs. VA in distal CRC p<0.0001; VA in proximal vs. AA in distal CRC p<0.0001; AA in proximal vs. VA in distal CRC p=0.021; AA in proximal CRC vs. AA in distal CRC p=0.006. Conclusion: The applicability of the CV test (a new-generation FIT) in CRC screening was confirmed. The AA reading was superior to VA (or SENSA) in its diagnostic accuracy in detecting proximal CRC patients. Distal CRCs were more accurately detected than proximal CRCs by both reading modes.
Further properties of fractional stochastic dominance

Abstract A continuum of stochastic dominance rules, also referred to as fractional stochastic dominance (SD), was introduced by Müller, Scarsini, Tsetlin, and Winkler (2017) to cover preferences from first- to second-order SD. Fractional SD can be used to explain many individual behaviors in economics. In this paper we introduce the concept of fractional pure SD, a special case of fractional SD. We investigate further properties of fractional SD, for example the generating processes of fractional pure SD via $\gamma$ -transfers of probability, Yaari’s dual characterization by utilizing the special class of distortion functions, the separation theorem in terms of first-order SD and fractional pure SD, Strassen’s representation, and bivariate characterization. We also establish several closure properties of fractional SD under quantile truncation, under comonotonic sums, and under distortion, as well as its equivalence characterization. Examples of distributions ordered in the sense of fractional SD are provided.
Decolonizing intervention assessment: Qualitative and interdisciplinary approaches to understanding “side effects”

In their focal article, Watts et al. (2021) argue for greater attention paid toward the unintended effects of organizational interventions, identifying barriers to their present realization, and suggesting relevant paths forward. Here, we propose the authors’ conceptualization of “side effects” may be the result of field-level biases plaguing industrial-organizational (I-O) psychology more broadly. For instance, in outlining seven “social forces that are to blame for this stagnation of knowledge” (p. XX), the focal authors insinuate that if only these social forces could be ameliorated, individual researchers could more readily address the stagnation of knowledge related to side-effects; we challenge that notion and further oppose the characterization of I-O psychologists as infallible victims of circumstance. Put another way, we propose that humble acknowledgement of our fallibility with respect to our intentions, our interpretations, and our methods of inquiry may more readily address the issue of side-effects. We argue that this acknowledgment suggests greater use of techniques that better integrate practitioner knowledge, organizational or community-specific evidence, and the perspectives or experiences of the affected community (Briner & Rousseau, 2011). Given our field’s tendency to prefer that which is quantifiable, we position qualitative methods as an undervalued methodology that is relevant to the present conversation and suggest that inductive research via qualitative methods is more tractable than currently suggested. Further, by adopting the perspectives of disciplines beyond I-O psychology that are more concretely committed to the decolonization of research methodologies, we suggest a more effective path forward to a broader understanding of organizational intervention assessment, including challenging what we ourselves consider to be side effects to begin with.
Performance evolution of mitigating transient execution attacks

Today's applications pay a performance penalty for mitigations to protect against transient execution attacks such as Meltdown [32] and Spectre [25]. Such a reduction in performance directly translates to higher operating costs and degraded user experience. This paper measures the performance impact of these mitigations across a range of processors from multiple vendors and across several security boundaries to identify trends over successive generations of processors and to attribute how much of the overall slowdown is caused by each individual mitigation. We find that overheads for operating system intensive workloads have declined by as much as 10×, down to about 3% on modern CPUs, due to hardware changes that eliminate the need for the most expensive mitigations. Meanwhile, a JavaScript benchmark reveals approximately 20% overhead persists today because mitigations for Spectre V1 and Speculative Store Bypass have not become more efficient. Other workloads like virtual machines and single-process, compute-intensive applications did not show significant slowdowns on any of the processors we measured.
Parallel block-delayed sequences

Programming languages using functions on collections of values, such as map, reduce, scan and filter, have been used for over fifty years. Such collections have proven to be particularly useful in the context of parallelism because such functions are naturally parallel. However, if implemented naively they lead to the generation of temporary intermediate collections that can significantly increase memory usage and runtime. To avoid this pitfall, many approaches use "fusion" to combine operations and avoid temporary results. However, most of these approaches involve significant changes to a compiler and are limited to a small set of functions, such as maps and reduces. In this paper we present a library-based approach that fuses widely used operations such as scans, filters, and flattens. In conjunction with existing techniques, this covers most of the common operations on collections. Our approach is based on a novel technique which parallelizes over blocks, with streams within each block. We demonstrate the approach by implementing libraries targeting multicore parallelism in two languages: Parallel ML and C++, which have very different semantics and compilers. To help users understand when to use the approach, we define a cost semantics that indicates when fusion occurs and how it reduces memory allocations. We present experimental results for a dozen benchmarks that demonstrate significant reductions in both time and space. In most cases the approach generates code that is near optimal for the machines it is running on.
Construction of value creation index system of dual-channel supply chain

This article analyzes the main body of the dual-channel supply chain value creation, based on the summary of the existing literature, defines the value creation of the online and offline dual-channel supply chain from three measurement dimensions, and divides the 15 measurement elements into hierarchical levels, find the path of influence between different elements, and provide a theoretical basis for the integration of the dual-channel supply chain.
Sharing is caring: secure and efficient shared memory support for MVEEs

Multi-Variant Execution Environments (MVEEs) are a powerful tool for protecting legacy software against memory corruption attacks. MVEEs employ software diversity to run multiple variants of the same program in lockstep, whilst providing them with the same inputs and comparing their behavior. Well-constructed variants will behave equivalently under normal operating conditions but diverge when under attack. The MVEE detects these divergences and takes action before compromised variants can damage the host system. Existing MVEEs replicate inputs at the system call boundary, and therefore do not support programs that use shared-memory IPC with other processes, since shared memory pages can be read from and written to directly without system calls. We analyzed modern applications, ranging from web servers, over media players, to browsers, and observe that they rely heavily on shared memory, in some cases for their basic functioning and in other cases for enabling more advanced functionality. It follows that modern applications cannot enjoy the security provided by MVEEs unless those MVEEs support shared-memory IPC. This paper first identifies the requirements for supporting shared-memory IPC in an MVEE. We propose a design that involves techniques to identify and instrument accesses to shared memory pages, as well as techniques to replicate I/O through shared-memory IPC. We implemented these techniques in a prototype MVEE and report our findings through an evaluation of a range of benchmark programs. Our contributions enable the use of MVEEs on a far wider range of programs than previously supported. By overcoming one of the major remaining limitations of MVEEs, our contributions can help to bolster their real-world adoption.
Remote OpenMP offloading

OpenMP has a long and successful history in parallel programming for CPUs, and more recently GPUs through accelerator offloading. In this work we show that the OpenMP accelerator offloading model is sufficient to seamlessly and efficiently utilize more than a single compute node, and its connected accelerators, without source code or compiler modifications. For applications that support multi-device offloading, any combination of local and remote CPUs and accelerators can be utilized simultaneously, fully transparent to the user. Our low-overhead implementation of Remote OpenMP Offloading is publicly available in the LLVM/OpenMP compiler infrastructure with LLVM 12 and later. To evaluate our work we provide detailed studies on micro benchmarks, as well as scaling results for two HPC proxy applications. We show perfect (weak) scaling across dozens of GPUs in multiple hosts with effectiveness that is directly proportional to the ratio of computation versus memory transfer time. Our work outlines the capabilities and limits of OpenMP 5.1 to efficiently utilize a distributed heterogeneous system without source, compiler, or language modifications.
CASE: a compiler-assisted SchEduling framework for multi-GPU systems

Modern computing platforms tend to deploy multiple GPUs on a single node to boost performance. GPUs have large computing capacities and are an expensive resource. Increasing their utilization without causing performance degradation of individual workloads is an important and challenging problem. Although services such as NVIDIA's MPS allow multiple cooperative kernels to simultaneously run on a single device, they do not solve the co-execution problem for uncooperative, independent kernels on such a multi-GPU system. To tackle this problem, we propose CASE --- a fully automated compiler-assisted scheduling framework. During the compilation of an application, CASE constructs GPU tasks from CUDA programs and instruments the code with a probe before each one. At runtime, each probe conveys information about its task's resource requirements such as memory and the number of streaming multiprocessor (SMs) needed to a user-level scheduler. The scheduler then places each task onto a suitable device by employing a policy appropriate to the system. In our prototype, a throughput-oriented scheduling policy is implemented to evaluate our resource-aware scheduling framework. The Rodinia benchmark suite and the Darknet neural network framework were used in our evaluation. The results show that, as compared to existing state-of-the-art methods, CASE improves throughput by up to 2.5X for Rodinia, and up to 2.7X for Darknet on modern NVIDIA GPU platforms, mainly due to the fact that it improves the average system utilization by up to 3.36X and the job turnaround time by up to 4.9X. Meanwhile, it limits individual kernel performance degradation within 2.5%. CASE achieved peak system utilization of 78% for Rodinia and 80% for Darknet on a 4XV100 system.
Vapro: performance variance detection and diagnosis for production-run parallel applications

Performance variance is a serious problem for parallel applications, which can cause performance degradation and make applications' behavior hard to understand. Therefore, detecting and diagnosing performance variance are of crucial importance for users and application developers. However, previous detection approaches either bring too large overhead and hurt applications' performance, or rely on nontrivial source code analysis that is impractical for production-run parallel applications. In this work, we propose Vapro, a performance variance detection and diagnosis framework for production-run parallel applications. Our approach is based on an important observation that most parallel applications contain code snippets that are repeatedly executed with fixed workload, which can be used for performance variance detection. To effectively identify these snippets at runtime even without program source code, we introduce State Transition Graph (STG) to track program execution and then conduct lightweight workload analysis on STG to locate variance. To diagnose the detected variance, Vapro leverages a progressive diagnosis method based on a hybrid model leveraging variance breakdown and statistical analysis. Results show that the performance overhead of Vapro is only 1.38% on average. Vapro can detect the variance in real applications caused by hardware bugs, memory, and IO. After fixing the detected variance, the standard deviation of the execution time is reduced by up to 73.5%. Compared with the state-of-the-art variance detection tool based on source code analysis, Vapro achieves 30.0% higher detection coverage.
Research on Strategy Optimization of 5G NSA Based on Mobile Learning

Abstract: With the rapid development of Internet education, according to the trend of educational technology integration under the new situation, the Ministry of Education proposes that making full use of modern educational resources and advanced technology, building a mobile learning environment, and making efficient use of 5G network environment for mobile learning and knowledge transmission are the development trends of future education. Firstly, this paper studies the key issues affecting speech perception. However, it analyzes the uplink power contraction of NSA voice terminal, and verifies and optimizes NSA voice quality strategy based on the experimental test data, so as to improve the voice perception quality within 5G NSA mobile learning environment network services.
A systematic analysis of the event-stream incident

On October 5, 2018, a GitHub user announced a critical security vulnerability in event-stream, a JavaScript package meant to simplify working with data-streams. The vulnerability, was introduced by a new maintainer, by including code designed to harvest account details from select Bitcoin wallets when executing as part of the Copay wallet. At the time of the incident, event-stream was used by hundreds of applications and averaged about two million downloads per week. This paper reports on the results of an independent analysis of the event-steam incident. A series of steps allowed the attacker to take control of important account functions, while the attack was designed to activate only on select few environments---only when part of a specific dependency tree, only on specific wallets, and only on the live Bitcoin network. Conventional program analysis techniques would have likely missed the attack, and manual vetting proved to be inadequate given the scale and complexity of dependencies typical of in modern applications. The event-stream incident provides an important case study of the risks associated with long and convoluted chains of third-party components, calling the research community to arms.
The Effect of U.S.-China Trade War on the Performance of U.S.-listed Chinese Firms

While numerous corporations seek high-quality capital in overseas markets, very little is known about the political risks embedded in this new wave of financial globalization. This research utilizes the trade dispute between the U.S. and China during 2017-2020 to examine how political risks harm the stock prices of firms listed in advanced stock markets. Using a difference-in-differences (DiD) analysis, this paper documents the negative impact of the trade war President Trump led which affects publicly listed Chinese stocks in the U.S. stock market. The findings demonstrate a salient effect of political risks, showing that the U.S. tariff policy shocks have a significant negative impact on the U.S.-listed Chinese stocks compared with the S&P 500 companies when policies have specific tariff target, leading to a 4.1%-12.6% underperformance of U.S.-listed Chinese stocks. However, this study does not find that the phase one deal, which de-escalates the situation, has a positive impact on the stock price of U.S.-listed Chinese firms.
Beating the I/O bottleneck: a case for log-structured virtual disks

With the increasing dominance of SSDs for local storage, today's network mounted virtual disks can no longer offer competitive performance. We propose a Log-Structured Virtual Disk (LSVD) that couples log-structured approaches at both the cache and storage layer to provide a virtual disk on top of S3-like storage. Both cache and backend store are order-preserving, enabling LSVD to provide strong consistency guarantees in case of failure. Our prototype demonstrates that the approach preserves all the advantages of virtual disks, while offering dramatic performance improvements over not only commonly used virtual disks, but the same disks combined with inconsistent (i.e. unsafe) local caching.
LiteReconfig: cost and content aware reconfiguration of video object detection systems for mobile GPUs

An adaptive video object detection system selects different execution paths at runtime, based on video content and available resources, so as to maximize accuracy under a target latency objective (e.g., 30 frames per second). Such a system is well suited to mobile devices with limited computing resources, and often running multiple contending applications. Existing solutions suffer from two major drawbacks. First, collecting feature values to decide on an execution branch is expensive. Second, there is a switching overhead for transitioning between branches and this overhead depends on the transition pair. LiteReconfig, an efficient and adaptive video object detection framework, addresses these challenges. LiteReconfig features a cost-benefit analyzer to decide which features to use, and which execution branch to run, at inference time. Furthermore, LiteReconfig has a content-aware accuracy prediction model, to select an execution branch tailored for frames in a video stream. We demonstrate that LiteReconfig achieves significantly improved accuracy under a set of varying latency objectives than existing systems, while maintaining up to 50 fps on an NVIDIA AGX Xavier board. Our code, with DOI, is available at https://doi.org/10.5281/zenodo.6345733.