Reviewerly/exHarmony huggingface dataset:
A Practical Intel SGX Setting for Linux Containers in the Cloud

With close to native performance, Linux containers are becoming the de facto platform for cloud computing. While various solutions have been proposed to secure applications and containers in the cloud environment by leveraging Intel SGX, most cloud operators do not yet offer SGX as a service. This is likely due to a number of security, scalability, and usability concerns coming from both cloud providers and users. Cloud operators worry about the security guarantees of unofficial SDKs, limited support for remote attestation within containers, limited physical memory for the Enclave Page Cache (EPC) making it difficult to support hundreds of enclaves, and potential DoS attacks against EPC by malicious users. Meanwhile, end users need to worry about careful program partitioning to reduce the TCB and adapting legacy applications to use SGX. We note that most of these concerns are the result of an incomplete infrastructure, from the OS to the application layer. We address these concerns with lxcsgx, which allows SGX applications to run inside containers while also: enabling SGX remote attestation for containerized applications, enforcing EPC memory usage control on a per-container basis, providing a general software TPM using SGX to augment legacy applications, and supporting partitioning with a GCC plugin. We then retrofit Nginx/OpenSSL and Memcached using the software TPM and SGX partitioning to defend against known and potential attacks. Thanks to the small EPC footprint of each enclave, we are able to run up to 100 containerized Memcached instances without EPC swapping. Our evaluation shows the overhead introduced by lxcsgx is less than 6.9% for simple SGX applications, 9.5% for Nginx/OpenSSL, and 20.9% for containerized Memcached.
Establishment of rule dictionary for efficient XACML policy management

In order to improve the evaluation efficiency of the XACML policy, the storage principle of the rule dictionary is analyzed and the XACML policy evaluation engine XDPMOE is proposed. This is a new XACML policy management optimization scheme based on bitmap storage and HashMap. First of all, we acquire numeralization policy set, establish the rule dictionary based on the array sequential storage structure, and use the rule dictionary to quickly index the policy rules to improve the efficiency of the policy evaluation. Secondly, bitmaps are used to store policy set, which reduces the space complexity of the engine. By simulating the arrival of the access request, the experimental results show that (1) By reordering the policy set, the time spent by the policy set in storing the bitmap is greatly reduced, and that (2) The average evaluation efficiency of XDPMOE has significantly improved compared to the Sun PDP, HPEngine and XEngine. The hash matching algorithm based on bitmap storage not only takes up less storage space, but also can improve the matching efficiency to a great extent.
Morphological and chemical assessment of alluvial gold grains from Ako'ozam and Njabilobe, southwestern Cameroon

Placer gold is widely distributed in southwestern Cameroon but no known lode sources have been uncovered possibly because of intense weathering. In this study, the morphology and microchemistry of alluvial gold from Ako'ozam and Njabilobe were investigated to understand the nature of the source mineralization. SEM, QEMSCAN and EMPA methods were employed to characterize morphology, mineral associations, alloy composition and mineral inclusions of placer gold in both localities. The gold grains exhibited angular to sub-rounded shapes, complex outlines and possessed surface textures such as striations, pits and cavities. These morphological parameters indicated a close source. Major mineral associations included clays and quartz with minor monazite, zircon, pyrite, ilmenite, rutile, chlorite and Fe oxide minerals. Gold in the area is essentially alloyed with Ag. Mineral inclusions included quartz, rutile, Fe-oxide and mica. Gold chemistry indicates interaction of the mineralization with basic rocks notably amphibolite and metadolerite in the area. The wide compositional span of Ag indicated that gold mineralization in the area resulted from multiple source deposit. Similarity in chemical character of gold from Ako'ozam and Njabilobe pointed to the same mineralizing event that sourced the placers in both localities. Gold chemistry suggested an orogenic style of mineralization in which gold occur as free gold in quartz veins. Liberation properties of gold within the Ako'ozam-Njabilobe area weighed in favor of physical gravity-based ore recovery methods which are environmentally friendly.
Spin-entanglement wave in a coarse-grained optical lattice

In the present work we explore a suitable coarse graining channel as a tool to describe the effective entanglement spreading in a coarse-grained spin-chain with different degrees of resolution. Comparing it with the experimental realizations performed with ultracold atoms, our results suggest that even if we are not able to fully resolve the system, entanglement can still be detected for some coarse graining levels. Furthermore, we show that it is possible to have some information about the "microscopic" entanglement, even if we have access only to the system's coarse-grained description. We show that the amount of entanglement decays exponentially with the lack of system resolution. The lack of experimental resolution might thus lead to an effective classical description.
Artificial Intelligence Technology Challenges Patent Laws

The artificial intelligence revolution has brought unprecedented challenges to today's ethical standards, legal rules, social order and public management systems. In terms of Patent law, the main challenge is the patent eligibility of the artificial intelligence invention. Computer technology is the most important technology in the field of artificial intelligence. However, since the 2014 Alice case, the US courts and USPTO have tended to deemed computer-related inventions that directed to abstract ideas that cannot meet the requirement of patent eligibility. In order to increase the possibility of patent entitled of artificial intelligence related inventions, patent applicants should should focus their construction of the claims to manifest what technical problems the invention are solving or identify the specific improvements that the claims are making.
Adaptive Irregular Graph Construction-Based Salient Object Detection

Saliency detection represents a vital pre-processing stage of computer vision. Most existing propagation-based salient object detection methods construct a k-regular graph for saliency propagation. Applying a regular graph to a vast smooth region is potentially prone to unnecessary or prolonged propagation errors, leading to the excessive highlighting of the background regions. To mitigate such problems, we substitute the conventional k-regular graph with an adaptive irregular graph for saliency value propagation, thereby avoiding unnecessary iterations over a vast smooth region. We first perform a clustering analysis based on the smoothness, color, and other features of regions. The new graph boosts an adaptive link density by considering the clustering result. In addition, we propose a seeding strategy for the propagation. Based on our experimental studies of six major benchmark datasets, our method performed favorably against the other state-of-the-art methods, both quantitatively and qualitatively.
A Novel Classification Method From the Perspective of Fuzzy Social Networks Based on Physical and Implicit Style Features of Data

Many practical scenarios have demanded that we should classify unlabeled data more accurately based on both physical features (e.g., color, distance, or similarity) and implicit style features of data. As most extant classification algorithms classify unlabeled data based only on their physical features, they become weak in achieving expected classification results for many scenarios. To work around this drawback in this paper, a novel classification method (FuCM) from the perspective of fuzzy social network based on both physical and implicit style features of data is proposed. Based on the proposed fuzzy social network and its dynamics about fuzzy influences of nodes, FuCM comprises two stages. In its training stage, after the fuzzy social network has been built, it learns the topological structure, reflecting physical features and implicit style features of data by carrying out fuzzy influence dynamics in the built network. In its prediction stage, both physical and implicit style features of data are effectively integrated to yield the double structure efficiency characterized by fuzzy influences of nodes. FuCM classifies unlabeled data according to the strongest connection measure based on the proposed double structure efficiency. FuCM does not assume that both data distribution and the classification by physical features or by both physical and implicit style features of data must be known in advance. Thus, it is a novel unified classification framework in this sense. In contrast to all the nine comparative methods, FuCM experimentally demonstrates its comparable classification performance on most synthetic, UCI and KEEL datasets, which can be well classified based only on physical features of data. Furthermore, it displays distinctive superiority on five case studies where satisfactory classification certainly depends on both physical and implicit style features.
Academic Venue Recommendations Based on Similarity Learning of an Extended Nearby Citation Network

The rapidly increasing number of potential academic venues for research publication and commentary has made sourcing the venue that would best contribute to promoting effective scientific cooperation more challenging. In this paper, we propose a similarity learning approach to determine the most appropriate venue to publish an article. We first analyze the article metadata and cited articles to build the citation network matrices of the given article and then apply these to learn and build similarity matrices between academic objects (i.e., articles, authors, and venues) at an extended nearby article citation network. Using the formed matrices, we estimate a collaborative anticipation confidence score of a relationship between the venues in the extended network. For our empirical studies, we used an actual academic dataset to validate the efficiency of our approach and recommend an appropriate academic venue. The experimental results highlight the effectiveness of our proposed approach to optimize overall recommendation quality, compared with other baseline approaches.
From patch-level to ROI-level deep feature representations for breast histopathology classification

We propose a framework for learning feature representations for variable-sized regions of interest (ROIs) in breast histopathology images from the convolutional network properties at patch-level. The proposed method involves fine-tuning a pre-trained convolutional neural network (CNN) by using small fixed-sized patches sampled from the ROIs. The CNN is then used to extract a convolutional feature vector for each patch. The softmax probabilities of a patch, also obtained from the CNN, are used as weights that are separately applied to the feature vector of the patch. The final feature representation of a patch is the concatenation of the class-probability weighted convolutional feature vectors. Finally, the feature representation of the ROI is computed by average pooling of the feature representations of its associated patches. The feature representation of the ROI contains local information from the feature representations of its patches while encoding cues from the class distribution of the patch classification outputs. The experiments show the discriminative power of this representation in a 4-class ROI-level classification task on breast histopathology slides where our method achieved an accuracy of 66.8% on a data set containing 437 ROIs with different sizes.
Obstacle Avoidance through Gesture Recognition: Business Advancement Potential in Robot Navigation Socio-Technology

Summary In the present modern age, a robot works like human and is controlled in such a manner that its movements should not create hindrance in human activities. This characteristic involves gesture feat and gesture recognition. This article is aimed to describe the developments in algorithms devised for obstacle avoidance in robot navigation which can open a new horizon for advancement in businesses. For this purpose, our study is focused on gesture recognition to mean socio-technological implication. Literature review on this issue reveals that movement of robots can be made efficient by introducing gesture-based collision avoidance techniques. Experimental results illustrated a high level of robustness and usability of the Gesture recognition (GR) system. The overall error rate is almost 10%. In our subjective judgment, we assume that GR system is very well-suited to instruct a mobile service robot to change its path on the instruction of human.
Source smartphone identification by exploiting encoding characteristics of recorded speech

Source device identification has become a hot topic in multimedia forensics recently. In this paper, a novel method is proposed for source smartphone identification by using encoding characteristics as the intrinsic fingerprint of recording devices. The encoding characteristics for the smartphones of 24 popular models derived from 7 mainstream brands are investigated and statistical features of some important parameters are extracted as the discriminative features for the smartphone identification. To keep a balance between reasonable feature dimension and high classification rate, a two-step feature selection strategy consisting of Variance Threshold and SVM-RFE is designed to choose the optimal features. Experimental results show that the proposed method can achieve high identification rates of 97.89% and 98.04% for the live recorded database (CKC-SD) and the TIMIT recaptured database (TIMIT-RSD), respectively, and furthermore our scheme performs better when compared with two typical source identification approaches using recorded speeches. In addition, robustness of the proposed features is evaluated while confronting double compression attack.
Rapid and accurate energy models through calibration with IPMI and RAPL

Summary Energy consumption in Cloud and High Performance Computing platforms is a significant issue and affects aspects such as the cost of energy and the cooling of the data center. Host level monitoring and prediction provides the groundwork for improving energy efficiency through the placement of workloads. Monitoring must be fast and efficient without unnecessary overhead, to enable scalability. This precludes the use of Watt meters attached per host, requiring alternative approaches such as integrated measurements and models. IPMI and RAPL are subject to error and partial measurement, which may be mitigated. Models allow for prediction and more responsive measures of power consumption, but require calibrating. The causes of calibration error are discussed, along with mitigation strategies, without overly complicating the underlying model. An outcome is a Watt meter emulator that provides hosts level power measurement along with estimated power consumption for a given workload, with an average error of 0.20W.
Forensic analysis of Nucleus RTOS on MTK smartwatches

Embedded personal devices as smartwatches can be a valuable source of information for the investigation of criminal acts, as they can store contact data, call records, instant messages, multimedia files and so, without requiring access to the connected smartphone. This paper presents the acquisition and forensic analysis done on different non-android smartwatches equipped with a low-cost MTK chip. In this article, we will present the results of a complete study about the information contained in these smartwatch models, showing those artifacts that could be stored inside and might be critical in the investigation of a criminal act.
Effective convolution method for privacy preserving in cloud over big data using map reduce framework

Cloud computing is used to connect several number of remote servers through Internet to accumulate and recover large data anywhere and anytime. As of the conventional privacy defending process, there is a possibility for malevolent assault on the sensitive information accumulated in the cloud. In this research, the authors have proposed a competent large data convert among privacy defending by Hadoop map reduce in the cloud. The procedure exploits fuzzy C-means clustering (FCM) algorithm grouping the data. For dimensionality reduction, map reduce framework will be used. In evaluation module, the recommended technique performed with the aid of K-nearest neighbour (KNN) classification algorithm in this phase using KNN technique to check the convolution process based on the threshold value, which is improving the utility of the privacy data. The consequence acquired illustrates that authors’ proposed scheme has enhanced the clustering exactness and also accomplishes the effectual convolution procedure to improve the privacy. From the experimental results, the proposed research achieved an effective clustering accuracy 76.07% and the existing K-means approach gets the clustering accuracy of 73.5% which is minimum value when compared to the proposed researches. The suggested technique is implemented in JAVA with Cloud Sim platform.
Spy in the Crowd: How User’s Privacy Is Getting Affected with the Integration of Internet of Thing’s Devices

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IoT Security Issues and Light Weight Block Cipher

Internet of things (IoT) has received much attention from many fields, and its applications are widespread in various domains. As many devices are connected to the Internet, the security issues arise and need to be addressed. In this paper, we review the challenges and the current status of IoT security technologies. Since most IoT devices operate on battery power or self-harvested energy sources, it is critical to reduce energy consumption for device operations. Therefore, we review some of light weight block cipher algorithms, which are one of the fundamental building blocks of countermeasures to cope with IoT security threats.
Per-Application Power Delivery

Datacenter servers are often under-provisioned for peak power consumption due to the substantial cost of providing power. When there is insufficient power for the workload, servers can lower voltage and frequency levels to reduce consumption, but at the cost of performance. Current processors provide power limiting mechanisms, but they generally apply uniformly to all CPUs on a chip. For servers running heterogeneous jobs, though, it is necessary to differentiate the power provided to different jobs. This prevents interference when a job may be throttled by another job hitting a power limit. While some recent CPUs support per-CPU power management, there are no clear policies on how to distribute power between applications. Current hardware power limiters, such as Intel's RAPL throttle the fastest core first, which harms high-priority applications.
Forensic analysis of Microsoft Skype for Business

We present three case studies to illustrate a methodology for conducting forensics investigation on Microsoft Skype for Business. The proposed methodology helps to retrieve information on chat and audio communications made by any account who accessed the PC, to retrieve IP addresses and communication routes for all the participants of a call, and to retrieve forensics evidence to identify the end-user devices of a VoIP call by analyzing the CODECs exchanged by the clients during the SIP (Session Initiation Protocol) handshaking phase. This information may help the investigator either to corroborate or to contradict an investigative hypothesis.
Correlating process and product data to get an insight into translation difficulty

Research in the field of translation studies suggests that translation product features can indicate translation difficulty.In the current pilot study, we investigate three of these features, namely the number of errors made in a translation, word translation entropy and degree of syntactic equivalence.We correlate these translation product features with translation process features that can be put together into three categories: duration, revision and gaze information.These features serve as a proxy for the cognitive effort required to solve difficulties in translation.The data that we used was gathered from professional translators as well as students of translation studies.The product data contains manual error annotations of the translations, automatically calculated entropy values and syntactic re-ordering metrics.The process data are derived from keystroke and eye-tracking data gathered during the translation process.By correlating product and process data, we inspect how translation difficulty is reflected in the translation process and whether it is feasible to use product features to predict difficulties in translation.In addition, we also compare data of professional translators and students.We will show that correlations between process and product features exist, which opens many doors to further research on translatability.
Deadline-Aware Cost Optimization for Spark

We present OptEx, a closed-form model of job execution on Apache Spark, a popular parallel processing engine. To the best of our knowledge, OptEx is the first work that analytically models job completion time on Spark. The model can be used to estimate the completion time of a given Spark job on a cloud, with respect to the size of the input dataset, the number of iterations, and the number of nodes comprising the underlying cluster. Experimental results demonstrate that OptEx yields a mean relative error of 6 percent in estimating the job completion time. Furthermore, the model can be applied for estimating the cost-optimal cluster composition for running a given Spark job on a cloud under a completion deadline specified in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SLO</i> (i.e., Service Level Objective). We show experimentally that OptEx is able to correctly estimate the required cluster composition for running a given Spark job under a given SLO deadline with an accuracy of 98 percent. We also provide a tool which can classify Spark jobs into job categories based on bisimilarity analysis on lineage graphs collected from the given jobs.
Personalized Online-to-Offline (O2O) Service Recommendation Based on a Novel Frequent Service-Set Network

Rapid development of information technology allows consumers to search and purchase services or products online, and then redeem them in offline stores. This emerging e-commerce model, the online-to-offline (O2O) service platform, has been catching significantly increasing attention. However, the numerous cyber O2O services lead to difficulties for customers to make appropriate decisions. In this paper, we propose a personalized O2O service recommendation method based on a novel frequent service-set network (FSSN). This method integrates the service network attribute based on FSSN and service essential attributes, which include popularity and commonality of service itself, and then generates a service recommendation list, which largely addresses the problem of sparse matrix. The experimental results show that FSSN-based recommendation has better performance than similarity-based recommendation methods and state-of-the-art matrix methods. Meanwhile, we find the relative importance degree of service network attribute and service essential attributes, and find the optimized combination of popularity performance and commonality performance to form service essential attributes with different matrix densities.