Profile inference revisited

Profile-guided optimization (PGO) is an important component in modern compilers. By allowing the compiler to leverage the program’s dynamic behavior, it can often generate substantially faster binaries. Sampling-based profiling is the state-of-the-art technique for collecting execution profiles in data-center environments. However, the lowered profile accuracy caused by sampling fully optimized binary often hurts the benefits of PGO; thus, an important problem is to overcome the inaccuracy in a profile after it is collected. In this paper we tackle the problem, which is also known as profile inference and profile rectification . We investigate the classical approach for profile inference, based on computing minimum-cost maximum flows in a control-flow graph, and develop an extended model capturing the desired properties of real-world profiles. Next we provide a solid theoretical foundation of the corresponding optimization problem by studying its algorithmic aspects. We then describe a new efficient algorithm for the problem along with its implementation in an open-source compiler. An extensive evaluation of the algorithm and existing profile inference techniques on a variety of applications, including Facebook production workloads and SPEC CPU benchmarks, indicates that the new method outperforms its competitors by significantly improving the accuracy of profile data and the performance of generated binaries.

An explainable assistant for multiuser privacy

Multiuser Privacy (MP) concerns the protection of personal information in situations where such information is co-owned by multiple users. MP is particularly problematic in collaborative platforms such as online social networks (OSN). In fact, too often OSN users experience privacy violations due to conflicts generated by other users sharing content that involves them without their permission. Previous studies show that in most cases MP conflicts could be avoided, and are mainly due to the difficulty for the uploader to select appropriate sharing policies. For this reason, we present ELVIRA, the first fully explainable personal assistant that collaborates with other ELVIRA agents to identify the optimal sharing policy for a collectively owned content. An extensive evaluation of this agent through software simulations and two user studies suggests that ELVIRA, thanks to its properties of being role-agnostic, adaptive, explainable and both utility- and value-driven, would be more successful at supporting MP than other approaches presented in the literature in terms of (i) trade-off between generated utility and promotion of moral values, and (ii) users’ satisfaction of the explained recommended output.

Pathological Laughter Prodromal to a Stroke of the Head of the Left Caudate Nucleus

A 38-year-old right-handed female with a past history of intermittent painful rash, dizzy spells, and chronic daily headaches as well as episodic migraines experienced an episode of transient involuntary pathological laughter, right arm weakness, and expressive dysphasia. She was found on MRI to have multiple strokes in multiple vascular distributions, including one in the head of the left caudate. A cardiac ECHO found an atrial myxoma, with extensive evaluation for other causes of stroke unrevealing. The differential diagnosis for pathological laughter in this patient is discussed. The most plausible cause in this patient is an infarct to the head of the left caudate nucleus caused by an embolus of the atrial myxoma.

A Lightweight Passive Human Tracking Method Using Wi-Fi

Target tracking is a critical technique for localization in an indoor environment. Current target-tracking methods suffer from high overhead, high latency, and blind spots issues due to a large amount of data needing to be collected or trained. On the other hand, a lightweight tracking method is preferred in many cases instead of just pursuing accuracy. For this reason, in this paper, we propose a Wi-Fi-enabled Infrared-like Device-free (WIDE) method for target tracking to realize a lightweight target-tracking method. We first analyze the impact of target movement on the physical layer of the wireless link and establish a near real-time model between the Channel State Information (CSI) and human motion. Secondly, we make full use of the network structure formed by a large number of wireless devices already deployed in reality to achieve the goal. We validate the WIDE method in different environments. Extensive evaluation results show that the WIDE method is lightweight and can track targets rapidly as well as achieve satisfactory tracking results.

Extensive Evaluation of the Overall Workplace Experience and Job Satisfaction of Dentists in Saudi Arabia

Background. Working conditions, job satisfaction, and their overall impact on a dentist’s job satisfaction are critical for future employment and retaining of dentists. Objectives. This study is aimed at determining the factors influencing the job satisfaction level among dentists. It is also aimed at evaluating how personal (age, gender) and professional (type, type of qualification, and year of practice) characteristics influence overall job satisfaction. Methods. For data collection, a structured self-administered questionnaire was used, in which one part collected information on personal characteristics. At the same time, the other contained a questionnaire related to job satisfaction. The German validated version of the questionnaire had a 10-point Warr-Cook-Wall (WCW) scale developed by Warr et al. in 1979. Each item was rated on a 5-point Likert scale, with 5 representing excessive satisfaction and 1 representing extreme dissatisfaction. Results. The results revealed that dentists in Saudi Arabia have a higher satisfaction level with “colleagues and fellow workers” (26.5%). The relation between the years of practice was highly significant. However, they were dissatisfied with their “income” (22.6%), and when compared with concerning age, gender, profession, and their practice years, this finding was highly significant. Conclusion. A higher percentage of satisfaction was seen with the “fellow and colleague’s workers” dimension. At the same time, “income” was the aspect with which the dentists showed extreme dissatisfaction.

Dental fractures: types, causes, and treatment

Studies indicate that the management of dental fractures might be challenging because many cases present with minor, hard-to-discover traumas, requiring extensive evaluation and diagnostic procedures. Accordingly, adequate management can only be achieved by establishing a proper diagnosis, drawing an adequate treatment plan, and conducting regular follow-up appointments. We have discussed the different causes, types, and treatments of dental fractures. The commonest causes include impaction by trauma, whether direct or indirect. Many traumatic events were reported in the literature, and age and carious lesions are important factors to consider. Many types of dental fractures were also reported in the literature, including root fractures, crown root fractures, enamel-dentin fractures with and without pulp exposure or crown fractures, enamel infractions, and fractures. Other types include avulsion, intrusion, extrusion, lateral luxation, concussion or subluxation, splinting, and alveolar segment fractures. The treatment should be integrated based on the fracture type, and adequate follow-up should be established to achieve the best predictive outcomes.

Compilation and evaluation of the Spanish SatiCorpus 2021 for satire identification using linguistic features and transformers

Satirical content on social media is hard to distinguish from real news, misinformation, hoaxes or propaganda when there are no clues as to which medium these news were originally written in. It is important, therefore, to provide Information Retrieval systems with mechanisms to identify which results are legitimate and which ones are misleading. Our contribution for satire identification is twofold. On the one hand, we release the Spanish SatiCorpus 2021, a balanced dataset that contains satirical and non-satirical documents. On the other hand, we conduct an extensive evaluation of this dataset with linguistic features and embedding-based features. All feature sets are evaluated separately and combined using different strategies. Our best result is achieved with a combination of the linguistic features and BERT with an accuracy of 97.405%. Besides, we compare our proposal with existing datasets in Spanish regarding satire and irony.

An Efficient Adaptive Fuzzy Hierarchical Sliding Mode Control Strategy for 6 Degrees of Freedom Overhead Crane

<pre>The paper proposes a new approach to efficiently control a three-dimensional overhead crane with six degrees of freedom (DoF). In addition to five usual output variables including three positions of the trolley, bridge and pulley and two swing angles of the hoisting cable, it is proposed to consider elasticity of the hoisting cable, which causes oscillation in the cable direction. That is, there exists $6^{th}$ under-actuated output in the crane system. To design an efficient controller for the six-DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes sway and oscillation of the overhead crane when it transports a payload to desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in the synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness.</pre>

An Efficient Adaptive Fuzzy Hierarchical Sliding Mode Control Strategy for 6 Degrees of Freedom Overhead Crane

<pre>The paper proposes a new approach to efficiently control a three-dimensional overhead crane with six degrees of freedom (DoF). In addition to five usual output variables including three positions of the trolley, bridge and pulley and two swing angles of the hoisting cable, it is proposed to consider elasticity of the hoisting cable, which causes oscillation in the cable direction. That is, there exists $6^{th}$ under-actuated output in the crane system. To design an efficient controller for the six-DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes sway and oscillation of the overhead crane when it transports a payload to desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in the synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness.</pre>

On Directed Densest Subgraph Discovery

Given a directed graph G , the directed densest subgraph (DDS) problem refers to the finding of a subgraph from G , whose density is the highest among all the subgraphs of G . The DDS problem is fundamental to a wide range of applications, such as fraud detection, community mining, and graph compression. However, existing DDS solutions suffer from efficiency and scalability problems: on a 3,000-edge graph, it takes three days for one of the best exact algorithms to complete. In this article, we develop an efficient and scalable DDS solution. We introduce the notion of [ x , y ]-core, which is a dense subgraph for G , and show that the densest subgraph can be accurately located through the [ x , y ]-core with theoretical guarantees. Based on the [ x , y ]-core, we develop exact and approximation algorithms. We further study the problems of maintaining the DDS over dynamic directed graphs and finding the weighted DDS on weighted directed graphs, and we develop efficient non-trivial algorithms to solve these two problems by extending our DDS algorithms. We have performed an extensive evaluation of our approaches on 15 real large datasets. The results show that our proposed solutions are up to six orders of magnitude faster than the state-of-the-art.