nils-herrmann/clean_scirepeval huggingface dataset:
Software protection for dynamically-generated code

Process-level Virtual machines (PVMs) often play a crucial role in program protection. In particular, virtualization-based tools like VMProtect and CodeVirtualizer have been shown to provide desirable obfuscation properties (i.e., resistance to disassembly and code analysis). To be efficient, many tools cache frequently-executed code in a code cache. This code is run directly on hardware and consequently may be susceptible to unintended, malicious modification after it has been generated.
 To thwart such modifications, this work presents a novel methodology that imparts tamper detection at run time to PVM-protected applications. Our scheme centers around the run-time creation of a network of software knots (an instruction sequence that checksums portions of the code) to detect tamper. These knots are used to check the integrity of cached code, although our techniques could be applied to check any software-protection properties. Used in conjunction with established static techniques, our solution provides a mechanism for protecting PVM-generated code from modification.
 We have implemented a PVM system that automatically inserts code into an application to dynamically generate polymorphic software knots. Our experiments show that PVMs do indeed provide a suitable platform for extending guard protection, without the addition of high overheads to run-time performance and memory. Our evaluations demonstrate that these knots add less than 10% overhead while providing frequent integrity checks.
Towards on Cloud of Things: Survey, Challenges, Open Research Issues, and Tools

In computer, Cloud of Things (CoT) it is a Technique came by integrated two concepts Internet of Things(IoT) and Cloud Computing. Therefore, Cloud of Things is a currently a wide area of research and development. This paper discussed the concept of Cloud of Things (CoT) in detail and explores the challenges, open research issues, and various tools that can be used with Cloud of Things (CoT). As a result, this paper gives a knowledge and platform to explore Cloud of Things (CoT), and it gives new ideas for researchers to find the open research issues and solution to challenges.
A Network-Centric Hardware/Algorithm Co-Design to Accelerate Distributed Training of Deep Neural Networks

Training real-world Deep Neural Networks (DNNs) can take an eon (i.e., weeks or months) without leveraging distributed systems. Even distributed training takes inordinate time, of which a large fraction is spent in communicating weights and gradients over the network. State-of-the-art distributed training algorithms use a hierarchy of worker-aggregator nodes. The aggregators repeatedly receive gradient updates from their allocated group of the workers, and send back the updated weights. This paper sets out to reduce this significant communication cost by embedding data compression accelerators in the Network Interface Cards (NICs). To maximize the benefits of in-network acceleration, the proposed solution, named INCEPTIONN (In-Network Computing to Exchange and Process Training Information Of Neural Networks), uniquely combines hardware and algorithmic innovations by exploiting the following three observations. (1) Gradients are significantly more tolerant to precision loss than weights and as such lend themselves better to aggressive compression without the need for the complex mechanisms to avert any loss. (2) The existing training algorithms only communicate gradients in one leg of the communication, which reduces the opportunities for in-network acceleration of compression. (3) The aggregators can become a bottleneck with compression as they need to compress/decompress multiple streams from their allocated worker group. To this end, we first propose a lightweight and hardware-friendly lossy-compression algorithm for floating-point gradients, which exploits their unique value characteristics. This compression not only enables significantly reducing the gradient communication with practically no loss of accuracy, but also comes with low complexity for direct implementation as a hardware block in the NIC. To maximize the opportunities for compression and avoid the bottleneck at aggregators, we also propose an aggregator-free training algorithm that exchanges gradients in both legs of communication in the group, while the workers collectively perform the aggregation in a distributed manner. Without changing the mathematics of training, this algorithm leverages the associative property of the aggregation operator and enables our in-network accelerators to (1) apply compression for all communications, and (2) prevent the aggregator nodes from becoming bottlenecks. Our experiments demonstrate that INCEPTIONN reduces the communication time by 70.9~80.7% and offers 2.2~3.1x speedup over the conventional training system, while achieving the same level of accuracy.
Twin fetus in fetu in a child: a case report and review of the literature

Fetus in fetu is an extremely rare condition wherein a malformed fetus is found in the abdomen of its twin. This entity is differentiated from teratoma by its embryological origin, its unusual location in the retroperitoneal space, and the presence of vertebral organization with limb buds and well-developed organ systems. The literature cites less than 100 cases worldwide of twin fetus in fetu.



A two-and-a-half-month-old Asian Indian baby boy had two malformed fetuses in his abdomen. The pre-operative diagnosis was made by performing an ultrasound and a 64-slice computer tomography scan of the baby's abdomen. Two fetoid-like masses were successfully excised from the retroperitoneal area of his abdomen. A macroscopic examination, an X-ray of the specimen after operation, and the histological features observed were suggestive of twin fetus in fetu.



Fetus in fetu is an extremely rare condition. Before any operation is carried out on a patient, imaging studies should first be conducted to differentiate this condition from teratoma. Surgical excision is a curative procedure, and a macroscopic examination of the sac should be done after twin or multiple fetus in fetu are excised.
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning

Robust query optimization becomes illusory in the presence of correlated predicates or user-defined functions. Occasionally, the query optimizer will choose join orders whose execution time is by many orders of magnitude higher than necessary. We present SkinnerDB, a novel database management system that is designed from the ground up for reliable optimization and robust performance. SkinnerDB implements several adaptive query processing strategies based on reinforcement learning. We divide the execution of a query into small time periods in which different join orders are executed. Thereby, we converge to optimal join orders with regret bounds, meaning that the expected difference between actual execution time and time for an optimal join order is bounded. To the best of our knowledge, our execution strategies are the first to provide comparable formal guarantees. SkinnerDB can be used as a layer on top of any existing database management system. We use optimizer hints to force existing systems to try out different join orders, carefully restricting execution time per join order and data batch via timeouts. We choose timeouts according to an iterative scheme that balances execution time over different timeouts to guarantee bounded regret. Alternatively, SkinnerDB can be used as a standalone, featuring an execution engine that is tailored to the requirements of join order learning. In particular, we use a specialized multi-way join algorithm and a concise tuple representation to facilitate fast switches between join orders. In our demonstration, we let participants experiment with different query types and databases. We visualize the learning process and compare against baselines.  Immanuel Trummer, Samuel Moseley, Deepak Maram, Saehan Jo, Joseph Antonakakis.  2018.  https://doi.org/10.14778/3229863.3236263  To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org.
Conv-RAM: An energy-efficient SRAM with embedded convolution computation for low-power CNN-based machine learning applications

Convolutional neural networks (CNN) provide state-of-the-art results in a wide variety of machine learning (ML) applications, ranging from image classification to speech recognition. However, they are very computationally intensive and require huge amounts of storage. Recent work strived towards reducing the size of the CNNs:  proposes a binary-weight-network (BWN), where the filter weights (wi's) are ±1 (with a common scaling factor per filter: α). This leads to a significant reduction in the amount of storage required for the Wi's, making it possible to store them entirely on-chip. However, in a conventional all-digital implementation [2, 3], reading the wjis and the partial sums from the embedded SRAMs require a lot of data movement per computation, which is energy-hungry. To reduce data-movement, and associated energy, we present an SRAM-embedded convolution architecture , which does not require reading the wi's explicitly from the memory. Prior work on embedded ML classifiers have focused on 1b outputs  or a small number of output classes , both of which are not sufficient for CNNs. This work uses 7b inputs/outputs, which is sufficient to maintain good accuracy for most of the popular CNNs . The convolution operation is implemented as voltage averaging , since the wj's are binary, while the averaging factor (1/N) implements the weight-coefficient α (with a new scaling factor, M, implemented off-chip).
Do Less and Achieve More: Training CNNs for Action Recognition Utilizing Action Images from the Web

Recently, attempts have been made to collect millions of videos to train CNN models for action recognition in videos. However, curating such large-scale video datasets requires immense human labor, and training CNNs on millions of videos demands huge computational resources. In contrast, collecting action images from the Web is much easier and training on images requires much less computation. In addition, labeled web images tend to contain discriminative action poses, which highlight discriminative portions of a video’s temporal progression. We explore the question of whether we can utilize web action images to train better CNN models for action recognition in videos. We collect 23.8K manually filtered images from the Web that depict the 101 actions in the UCF101 action video dataset. We show that by utilizing web action images along with videos in training, significant performance boosts of CNN models can be achieved. We then investigate the scalability of the process by leveraging crawled web images (unfiltered) for UCF101 and ActivityNet. We replace 16.2M video frames by 393K unfiltered images and get comparable performance.
Citarasa engineering model for affective design of vehicles

Research on affective design has expanded considerably in recent years. The focus has primarily been on consumer products, such as mobile phones. Here we discuss a model for affective design of vehicles, with special emphasis on cars. It was developed in CATER - a research program intended to support mass customization of vehicles. Cars are different from consumer products because they represent major investments, and customers will typically take about a year to decide what to buy. During this time the customer goes through several motivational stages from belief to attitude to intention and behavior, which will affect his/her priorities. The model drives development of the citarasa engineering system.
Graph Bundling by Kernel Density Estimation

We present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved graph edges into the height gradient flow. Our technique can be easily and efficiently implemented using standard graphics acceleration techniques and produces graph bundlings of similar appearance and quality to state-of-the-art methods at a fraction of the cost. Additionally, we show how to create bundled layouts constrained by obstacles and use shading to convey information on the bundling quality. We demonstrate our method on several large graphs.
Real-Time Dense Monocular SLAM With Online Adapted Depth Prediction Network

Considerable advances have been achieved in estimating the depth map from a single image via convolutional neural networks (CNNs) during the past few years. Combining depth prediction from CNNs with conventional monocular simultaneous localization and mapping (SLAM) is promising for accurate and dense monocular reconstruction, in particular addressing the two long-standing challenges in conventional monocular SLAM: low map completeness and scale ambiguity. However, depth estimated by pretrained CNNs usually fails to achieve sufficient accuracy for environments of different types from the training data, which are common for certain applications such as obstacle avoidance of drones in unknown scenes. Additionally, inaccurate depth prediction of CNN could yield large tracking errors in monocular SLAM. In this paper, we present a real-time dense monocular SLAM system, which effectively fuses direct monocular SLAM with an online-adapted depth prediction network for achieving accurate depth prediction of scenes of different types from the training data and providing absolute scale information for tracking and mapping. Specifically, on one hand, tracking pose (i.e., translation and rotation) from direct SLAM is used for selecting a small set of highly effective and reliable training images, which acts as ground truth for tuning the depth prediction network on-the-fly toward better generalization ability for scenes of different types. A stage-wise Stochastic Gradient Descent algorithm with a selective update strategy is introduced for efficient convergence of the tuning process. On the other hand, the dense map produced by the adapted network is applied to address scale ambiguity of direct monocular SLAM which in turn improves the accuracy of both tracking and overall reconstruction. The system with assistance of both CPUs and GPUs, can achieve real-time performance with progressively improved reconstruction accuracy. Experimental results on public datasets and live application to obstacle avoidance of drones demonstrate that our method outperforms the state-of-the-art methods with greater map completeness and accuracy, and a smaller tracking error.
Host-parasitoid associations in Strepsiptera.

Strepsiptera are obligate endoparasitoids that exhibit extreme sexual dimorphism and parasitize seven orders and 33 families of Insecta. The adult males and the first instar larvae in the Mengenillidia and Stylopidia are free-living, whereas the adult females in Mengenillidia are free-living but in the suborder Stylopidia they remain endoparasitic in the host. Parasitism occurs at the host larval/nymphal stage and continues in a mobile host until that host's adult stage. The life of the host is lengthened to allow the male strepsipteran to complete maturation and the viviparous female to release the first instar larvae when the next generation of the host's larvae/nymphs has been produced. The ability of strepsipterans to parasitize a wide range of hosts, in spite of being endoparasitoids, is perhaps due to their unique immune avoidance system. Aspects of virulence, heterotrophic heteronomy in the family Myrmecolacidae, cryptic species, genomics, immune response, and behavior of stylopized hosts are discussed in this chapter.
Semantic background subtraction

We introduce the notion of semantic background subtraction, a novel framework for motion detection in video sequences. The key innovation consists to leverage object-level semantics to address the variety of challenging scenarios for background subtraction. Our framework combines the information of a semantic segmentation algorithm, expressed by a probability for each pixel, with the output of any background subtraction algorithm to reduce false positive detections produced by illumination changes, dynamic backgrounds, strong shadows, and ghosts. In addition, it maintains a fully semantic background model to improve the detection of camouflaged foreground objects. Experiments led on the CDNet dataset show that we managed to improve, significantly, almost all background subtraction algorithms of the CDNet leaderboard, and reduce the mean overall error rate of all the 34 algorithms (resp. of the best 5 algorithms) by roughly 50% (resp. 20%).
Investigating Information Systems Strategic Alignment

Although strategic alignment is an important theoretical concept, and achieving alignment or fit among organizational elements would seem to be an important business objective, empirical demonstrations of the importance of alignment have been uncommon (White 1986). This study attempted to provide an empirical assessment of the nature and importance of information systems strategic alignlnent There is much discussion today about the importance of 1. THE CONCEPTUAL MODEL strategic alignment in general (e.g., Venkatraman and Camillus 1984; White 1986) and information systems (IS) The conceptual model underlying the study is depicted in strategic alignment in particular (e.g., Elam et al. 1988;  It depicts the propositions that IS.strategic Henderson and Thomas 1992; Henderson and Venkatraman 1992; Keen 1991; Ward, Griffiths and Whitmore 1990). alignment, defined as the fit existing between business The discussion generally suggests that the better the alignstrategy and IS strategy, impacts performance at the IS ment, the better the expected company performance. level and at the overall business level. However, there have been few empirical verifications of this proposition (White 1986). In addition, the discussion The results of the overall study, and detailed descriptions of about alignment has tended to be imprecise and vague (Van the instruments used to measure business strategy, IS de Ven and Drazin 1985). Important objectives of this strategy, IS effectiveness and business performance, have study were to identify a useful, precise model for the IS been described in Chan et al. (1993) and Chan and Huff strategic alignment construct and to examine empirically (1992) respectively. (Table 1 provides a brief outline of relationships among IS strategic alignment; IS effectiveness, the overall study.) This paper discusses the derivation and and business performance. significance of the IS strategic alignment computations and The discussion opens with a brief overview of the concepthe study's findings regarding alignment tual model underlying this study. Next, instruments that measure business strategy and IS strategy are introduced. Then instruments that measure IS effectiveness and business performance are outlined. This is followed by a 2. MEASURING BUSINESS STRATEGY AND general discussion of various approaches that are used to INFORMATION SYSTEMS STRATEGY assess alignment. Specific models of alignment are evaluated and the primary model for IS strategic alignment used within this research is selected and described. The links In order to assess IS strategic alignment, there needed to be between IS strategic alignment and performance are then assessments of both business strategy and IS strategy. discussed. Finally, the paper concludes with a description Table 2 lists the dimensions of these constructs as operaof the study's implications for management and future tionalized in this study. Multiple indicators were used to research. measure each dimension.
An Integrated Expert User with End User in Technology Acceptance Model for Actual Evaluation

Effective evaluation is necessary in order to ensure systems adequately meet the requirements and information processing needs of the users and scope of the system. Technology acceptance model is one of the most popular and effective models for evaluation. A number of studies have proposed evaluation frameworks to aid in evaluation work. The end users for evaluation the acceptance of new technology or system have a lack of knowledge to examine and evaluate some features in the new technology/system. This will give a fake evaluation results of the new technology acceptance. This paper proposes a novel evaluation model to evaluate user acceptance of software and system technology by modifying the dimensions of the Technology Acceptance Model (TAM) and added additional success dimension for expert users. The proposed model has been validated by an empirical study based on a questionnaire. The results indicated that the expert users have a strong significant influence to help in evaluation and pay attention to some features that end users have lack of knowledge to evaluate it.
Two-Factor Cancelable Biometrics Authenticator

Biometrics-based authentication system offers advantages of providing high reliability and accuracy. However, the contemporary authentication system is impuissance to compromise. If a biometrics data is compromised, it cannot be replaced and rendered unusable. In this paper, a cancelable biometrics-based authenticator is proposed to solve this irrevocability issue. The proposed approach is a two-factor authentication system, which requires both of the random data and facial feature in order to access the system. In this system, tokenized pseudo-random data is coupled with moment-based facial feature via inner product algorithm. The output of the product is then discretized to generate a set of private binary code, coined as 2factor-Hashing code, which is acted as verification key. If this biometrics-based verification key is compromised, a new one can be issued by replacing a different set of random number via token replacement. Then, the compromised one is rendered completely useless. This feature offers an extra protection layer against biometrics fabrication since the verification code is replaceable. Experimental results demonstrate that the proposed system provides zero Equal Error Rate in which there is a clear separation in between the genuine and the imposter distribution populations.
A Software Process Ontology and Its Application

Software process is viewed as an important factor to deliver high quality products. Although there have been several Software Process Models proposed, the software processes are still short of formal descriptions. This paper presents an ontology-based approach to express software processes at the conceptual level. An OWL-based ontology for software processes, called SPO (Software Process Ontology), is designed, and it is extended to generate ontologies for specific process models, such as CMMI and ISO/IEC 15504. A prototype of a web-based process assessment tool based on SPO is developed to illustrate the advantages of this approach. Finally, some further research in this direction is outlined.