Reviewerly/exHarmony huggingface dataset:
Memory flipping: a threat to NUMA virtual machines in the Cloud

vNUMA is the most recent technology used by hypervisors to deal with Non Uniform Memory Access (NUMA) machines, which currently composed most datacenters. vNUMA consists in presenting to the virtual machine (VM) the initial mapping (at boot time) of its virtual resources to physical resources. By this way, all NUMA optimizations implemented by almost all VM’s OS (e.g. Linux) can become effective. However, in order to be effective itself, vNUMA imposes that the initial resource mapping of the VM should remain unchanged during the VM lifetime. Current hypervisors enforce this requirement by avoiding virtual resource migration (between different NUMA nodes, in the same machine), VM migration (between different machines), and memory ballooning.However, we found that memory flipping the most efficient network virtualization approach violates the above requirement. In other words, a VM which performs network operations leads the hypervisor implicitly performs memory page migrations. In this paper, we show that violating this requirement can degrade performance by up to 18%. We present two solutions which mitigate the issue. We prototype these solutions in Xen hypervisor, a popular open source hypervisor, which is widely used by Amazon Web Services. The evaluation results, performed with well known benchmarks, show that our two solutions are able to almost cancel the issue, while keeping memory flipping effective.
A study on factors affecting success of women entrepreneurs in tigray region-ethiopia (A case study of adigrat town entrepreneurs)

This study was conducted to identify the factors that affect success of women entrepreneurs in Tigray region, Adigrat town. The study employs descriptive survey research. The target population of the study was identified 400 women entrepreneurs running their enterprises successfully since 5 years in the town out of that 150 sample were taken by using simple random sampling method. In order to collect data questionnaire was distributed, then the collected data was subject to several statistical analysis using SPSS version 20. From the results the researchers concludes that factors having experience with their business, support from family, from past failures and motive to help their families are the factors that make them successful. Continuous support from SMEs office of the town was another success factor for the women.Keywords: Women, Entrepreneurs, success, Small and Micro Enterprises (SMEs)
Robot-to-Robot Relative Pose Estimation using Humans as Markers

In this paper, we propose a method to determine the 3D relative pose of pairs of communicating robots by using human pose-based key-points as correspondences. We adopt a 'leader-follower' framework, where at first, the leader robot visually detects and triangulates the key-points using the state-of-the-art pose detector named OpenPose. Afterward, the follower robots match the corresponding 2D projections on their respective calibrated cameras and find their relative poses by solving the perspective-n-point (PnP) problem. In the proposed method, we design an efficient person re-identification technique for associating the mutually visible humans in the scene. Additionally, we present an iterative optimization algorithm to refine the associated key-points based on their local structural properties in the image space. We demonstrate that these refinement processes are essential to establish accurate key-point correspondences across viewpoints. Furthermore, we evaluate the performance of the proposed relative pose estimation system through several experiments conducted in terrestrial and underwater environments. Finally, we discuss the relevant operational challenges of this approach and analyze its feasibility for multi-robot cooperative systems in human-dominated social settings and feature-deprived environments such as underwater.
A Faster Tree-Decomposition Based Algorithm for Counting Linear Extensions

We investigate the problem of computing the number of linear extensions of a given n-element poset whose cover graph has treewidth t. We present an algorithm that runs in time $${\tilde{O}}(n^{t+3})$$ for any constant t; the notation $${\tilde{O}}$$ hides polylogarithmic factors. Our algorithm applies dynamic programming along a tree decomposition of the cover graph; the join nodes of the tree decomposition are handled by fast multiplication of multivariate polynomials. We also investigate the algorithm from a practical point of view. We observe that the running time is not well characterized by the parameters n and t alone: fixing these parameters leaves large variance in running times due to uncontrolled features of the selected optimal-width tree decomposition. We compare two approaches to select an efficient tree decomposition: one is to include additional features of the tree decomposition to build a more accurate, heuristic cost function; the other approach is to fit a statistical regression model to collected running time data. Both approaches are shown to yield a tree decomposition that typically is significantly more efficient than a random optimal-width tree decomposition.
Augmenting Google Search in Ranking Twitter Users

Content recommendation, and other applications, may require a ranking of popular accounts relevant to a specific geographic area. Ranking requires well-formed city communities that are representative of the geographic area. This task is complicated due to Twitter users' inherently noisy locations. In this paper, we propose a novel method for establishing city-level communities by exploiting ties to known local celebrity accounts discovered via automatic Google searches. A modified TF-IDF measure is used to rank celebrity users that are influential in one city community and not others. This approach allows for a targeted collection that is orders of magnitude smaller than comparable approaches, but still achieves better performance.
Simple Domain Adaptation for CAD based Object Recognition

We present a simple method of domain adaptation between synthetic images and real images - by high quality rendering of the 3D models and correlation alignment. Using this method, we solve the problem of 3D object recognition in 2D images by fine-tuning existing pretrained CNN models for the object categories using the rendered images. Experimentally, we show that our rendering pipeline along with the correlation alignment improve the recognition accuracy of existing CNN based recognition trained on rendered images - by a canonical renderer - by a large margin. Using the same idea we present a general image classifier of common objects which is trained only on the 3D models from the publicly available databases, and show that a small number of training models are sufficient to capture different variations within and across the classes.
The Virtual Wall of the Fourth Amendment

The evolution of computing cannot be fully explained without consideration of the legal and social friction that challenged individuals at the frontier of computer development. The incorporation of the personal computer into nearly every American household has made the technology central to modern social interaction and has expanded daily life into cyberspace. How do fundamental laws, such as the Fourth Amendment, expand alongside this new media? With the ever-increasing virtualization of our lives, we will be faced with a whole array of legal conundrums and we can learn a great deal about how to alleviate these issues by recognizing the challenges of the past.
CSOD: context-sensitive overflow detection

Buffer overflow is possibly the most well-known memory issue. It can cause erratic program behavior, such as incorrect outputs and crashes, and can be exploited to issue security attacks. Detecting buffer overflows has drawn significant research attention for almost three decades. However, the prevalence of security attacks due to buffer overflows indicates that existing tools are still not widely utilized in production environments, possibly due to their high performance overhead or limited effectiveness. This paper proposes CSOD, a buffer overflow detection tool designed for the production environment. CSOD proposes a novel context-sensitive overflow detection technique that can dynamically adjust its detection strategy based on the behavior of different allocation calling contexts, enabling it to effectively detect overflows in millions of objects via four hardware watchpoints. It can correctly report root causes of buffer over-writes and over-reads, without any additional manual effort. Furthermore, CSOD only introduces 6.7% performance overhead on average, which makes it appealing as an always-on approach for production software.
Content Based Image Retrieval: A Review

Image recovery was one of the most thrilling and vibrant fields of computer vision science. Content-based image retrieval systems (CBIR) are used to catalog, scan, download and access image databases automatically. Color &amp; texture features are significant properties for content-based image recovery systems. The content-based image retrieval (CBIR) is therefore an attractive source of accurate and quick retrieval. Number of techniques has been established in recent years to improve the performance of CBIR. This paper discusses why CBIR is important nowadays along with the limitations and benefits. Apart from applications, various feature extraction techniques used in CBIR are also discussed.
Optimization Study of Parametric Thermal Bimetal Material Module for Green Building in Tropical Humid Climate

The largest energy consumption that we use is the consumption of electrical energy in terms of meeting the lighting needs and building air conditioning requirements (World Energy Consumption, 2016). According to the Ministry of Energy and Mineral Resources (2017), Indonesia’s largest energy use in commercial buildings is for flight systems (63%), lighting systems (20%), vertical transportation (7%) electronic devices (10%). The use of energy in the fulfillment of excessive needs result in worsening conditions on earth. Data can be a reflection of how the condition of the earth that we live at this time. Energy savings should be made to reduce the damage already occurring on this earth such as electricity usage savings, optimization of use of materials, the use of motor vehicles that cause air pollution, and others. The way that can be used to reduce artificial energy use is to utilize the existing passive building design such as the use of solar energy that can be maximized during the day, so that the use of electrical energy for lamps and artificial air conditioning. reduced. In addition, it can also be considered the optimal use of wind direction and speed that can suppress the use of Air Conditioner (AC) in excess. Building envelope with bimetal thermal material module application is part of kinetic architecture via biomimicry approach. Kinetic architecture is a concept where buildings are designed to allow parts of buildings to move without compromising the unity of the structural system. Approaches that can be applied in green building design is by optimizing bimetal material module that utilizes the thermal coefficient of a material. With this approach, building envelopes are improvised so that they can adapt to the existing environment. This research take location of case study in Kampung Juminahan, Yogyakarta, Indonesia which has characters of comunal housing.
Electronic payment system need of the Hour

In the latter, each receiver owns a secret private key and a publishable public key. In public-key cryptography, the sender finds the receiver's public key and uses it to encrypt the message, where as the receiver uses the private key to decrypt the message. Non-electronic payment methods (such as, using cash, writing a cheques, sending a money order, or giving your credit card number over the telephone) have several limitations in e-commerce environment. First, cash cannot be used for transactions because there is no face-to-face contact. They may be physically stolen or someone may steal our credit card number (from a receipt, over the phone, or from a Web site) and use our card to rack up debts. Credit cards can make life easier and be a great tool, but if they aren't used wisely they can become a huge financial burden. In this case, customer uses website provided by the bank. Customer logins to the bank's website and registers another bank account. He/she then places a request to transfer certain amount to that account. Online debit cards are considered superior because the PIN system has more security than signature system, which is used in offline debit cards. Smart cards are similar to credit cards and debit cards, except they store information on an embedded chip instead of on a magnetic strip on the back. Although credit cards dominate online payments today, electronic cash shows promise for the future.
Robust harmonic field based tooth segmentation in real-life noisy scanned mesh

Dental segmentation plays an important role in prosthetic dentistry such as crowns, implants and even orthodontics. Since people have different dental structures, it is hard to make a general dental segmentation model. Recently, there are only a few studies which try to tackle this problem. In this paper, we propose simple and intuitive algorithms for harmonic field based dental segmentation method to provide robustness for clinical dental mesh data. Our model includes additional grounds to gum, a pair of different Dirichlet boundary conditions, and convex segmentation for post-processing. Our data is generated for clinical usage and therefore has many noise, holes, and crowns. Moreover, some meshes have abraded teeth which deter the performance of harmonic field due to its dramatic gradient change. To the best of our knowledge, the proposed method and experiments are the first that deals with real clinical data containing noise and fragmented areas. We evaluate the results qualitatively and quantitatively to demonstrate the performance of the model. The model separates teeth from gum and other teeth very accurately. We use intersection over union (IoU) to calculate the overlap ratio between tooth. Moreover, human evaluation is used for measuring and comparing the performance of our segmentation model to other models. We compare the segmentation results of a baseline model and our model. Ablation study shows that our model improves the segmentation performance. Our model outperforms the baseline model at the expanse of some overlap which can be ignored.
Anxiety Effect: A Case of Text Modification and the Effect of High and Low Anxiety Levels on Medical Students’ Comprehension Performance

Introduction: The current study sought to investigate the impact of text modifications (lexically and grammatically modified) on reading comprehension ability of medical students with high and low levels of anxiety. Materials and Methods: To pursue the purpose of this study, 150 male and female medical students from Shahid Beheshti University of Medical Sciences participated in the study. The participants did not take a language proficiency test to ensure homogeneity, as pretesting might affect the internal and external validity of the study due to the interaction effect of pretesting [1]. The framework proposed by [2]. Moreover, a questionnaire developed by [3] entitled “Foreign Language Reading Anxiety Scale (FLRAS)” on a five-point Likert scale with 20 items served as an instrument. MANOVA was run to analyze the data. Results: The findings revealed that there was a significant difference between the reading comprehension ability of medical students with high and low levels of anxiety exposed to lexically modified (p<0.01), grammatically modified (p<0.01), lexically and grammatically modified (p<0.01), and unmodified passages (p<0.01). Conclusion: It is hoped that the findings from this study will guide researchers into new directions so that they may go on to discover profound insights about text simplification for medical students in Iran and all over the world.
An Entropy-Based Inverse Tone Mapping Operator with Improved Color Accuracy for High Dynamic Range Applications

Conversion of existing image and video content to High Dynamic Range (HDR) using inverse Tone Mapping Operators (iTMOs) is expected to open new market opportunities for studios and content owners. One of the issues facing iTMOs arises from the fact that changing the input Standard Dynamic Range (SDR) luminance to HDR results in color shifts, regardless of the color space or gamut that the operation takes place, requiring the use of an efficient color restoration method. Therefore, maintaining the color accuracy between the input SDR and output HDR content is of high importance in designing iTMOs. In this paper, we propose an iTMO capable of maintaining the color accuracy between the original SDR and the resulting HDR output, while at the same time providing a good balance between the overall brightness and contrast in the HDR content. The results of our subjective evaluation demonstrate that the colors of the generated HDR content closely match those of the original SDR input, outperforming the existing state-of-the-art iTMOs and making the resulting HDR look natural and more appealing to the viewers.
Low-and Semantic-Level Cues for Forensic Splice Detection

Image forensics is increasingly of interest, due to the deluge of online photo-sharing platforms and high-quality image editing software. While advances in computer vision enable these editing tools, they also provide a means by which to detect such tampering. Recent work in large-scale image phylogeny, for instance, aims to infer relationships between multiple images which may have contributed to a manipulation. A key task within this scope is detecting splicing between a pair of images, which we address with a combination of low-and semantic-level cues in order to provide fast detection with the false alarm rate demanded by large image collections. We show that, while deep learning approaches contribute to these ends, traditional feature-based detection still forms the basis of a useful detector, providing scale invariance without incurring the complexity associated with deep networks. Our method out-performs the state-of-the-art approach based on deep learning, measured on a challenge dataset for splice detection, and can also be used for detection of copy-move manipulations
The Device for Monitoring of Functional Status of Patients During Cardiac Rehabilitation

Nowadays, the issue of rehabilitation of patients with cardiovascular diseases, which are the most lethal diseases, of vital importance. Cardiac rehabilitation includes a variety of recovering activities such as physiotherapy. Physiotherapy involves physical exercises and Terrainkur and means a lot in revitalization. However, physiotherapeutic procedures must be supervised by a physician as well as organized properly and only in this way rehabilitation will bring direct results and fasten the recovery. At the same time, it is necessary to take into account the individual characteristics of the patient, since ill-conceived rehab exercise could cause unacceptable health risk up to death. Therefore, an important role in this process is played by the control of physiological data for objective assessment of the body state. The paper represents the development of a wireless module for the integrated support of monitoring the functional state of the body during cardiac rehabilitation.
Malicious DNS Traffic in Tor: Analysis and Countermeasures

Anonymization is commonly seen as useful only for people that have something to hide. Tor exit nodes are therefore associated with malicious behaviour and especially the so-called “darknet”. While the Tor network supports hidden services, and a large share of these serve illegal purposes, most of the traffic in the Tor network exits to the normal Internet and could be, and probably is, legal. We investigate this by taking a look at the DNS requests of a high-bandwidth exit node. We observe some malicious behaviour (especially DNS scans), questionable targets (both widely seen as immoral as well as very likely illegal in most countries), and careless usage. However, all these, while undoubtable undesirable, make up only a small share of the exit traffic. We then propose some additions to reduce the detected malicious use.
Getting Information Systems Programs Classified as STEM: A U.S.-based Perspective from an AIS Task Force Study and Panel Discussion

Science, technology, engineering, and math (STEM) is a designation for educational degree programs that have certain benefits that non-STEM programs do not. To achieve a STEM classification, a program must meet certain criteria. Programs in the natural sciences, engineering, and math departments often readily meet these criteria. However, although programs such as information systems, information technology, and business intelligence have technology at their core, these programs, especially in business colleges, often face difficulties in meeting the STEM criteria. In this paper, we review the STEM designation and provide insight into information systems and related degree programs that have received this designation based on findings from an AIS task force and an AMCIS panel discussion.
Cascaded Fault Diagnosis Based on Weighted Computer Network in Smart City Environment Considering Focusing Fuzzy Clustering Algorithm

In view of the cascaded fault diagnosis based on the weighted computer network in the smart city environment that needs to be solved urgently, the cascade relationship between the network nodes is studied on the basis of the focusing fuzzy clustering algorithm, and a kind of cascaded fault diagnosis based on the weighted computer network in the smart city environment considering the focusing fuzzy clustering algorithm is put forward. In this method, the influence of the fault identification and fault diagnosis behavior on the cascade metrics is taken into full consideration. The historical interaction evidence window, the timeliness factor and the penalty factor are introduce, and the aggregation method of the direct cascaded degree and the indirect cascaded degree is provided, which has improved the accuracy of the cascaded fault diagnosis based on the weighted computer network and effectively suppressed the threat of the abnormal entities. The simulation results show that compared with the traditional methods, the method put forward in this paper can diagnose the cascaded degree effectively and has good dynamic adaptability at the same time.
Efficient Inner Product Approximation in Hybrid Spaces.

Many emerging use cases of data mining and machine learning operate on large datasets with data from heterogeneous sources, specifically with both sparse and dense components. For example, dense deep neural network embedding vectors are often used in conjunction with sparse textual features to provide high dimensional hybrid representation of documents. Efficient search in such hybrid spaces is very challenging as the techniques that perform well for sparse vectors have little overlap with those that work well for dense vectors. Popular techniques like Locality Sensitive Hashing (LSH) and its data-dependent variants also do not give good accuracy in high dimensional hybrid spaces. Even though hybrid scenarios are becoming more prevalent, currently there exist no efficient techniques in literature that are both fast and accurate. In this paper, we propose a technique that approximates the inner product computation in hybrid vectors, leading to substantial speedup in search while maintaining high accuracy. We also propose efficient data structures that exploit modern computer architectures, resulting in orders of magnitude faster search than the existing baselines. The performance of the proposed method is demonstrated on several datasets including a very large scale industrial dataset containing one billion vectors in a billion dimensional space, achieving over 10x speedup and higher accuracy against competitive baselines.