Query: RELATIONSHIP OF PERSONALITY TRAITS AND COUNTERPRODUCTIVE WORK BEHAVIORS : THE MEDIATING EFFECTS OF JOB SATISFACTION

Relevant passages:
Relationship of personality to performance motivation: a meta-analytic review. <=SEP=> This article provides a meta-analysis of the relationship between the five-factor model of personality and 3 central theories of performance motivation (goal-setting, expectancy, and self-efficacy motivation). The quantitative review includes 150 correlations from 65 studies. Traits were organized according to the five-factor model of personality. Results indicated that Neuroticism (average validity = -.31) and Conscientiousness (average validity = .24) were the strongest and most consistent correlates of performance motivation across the 3 theoretical perspectives. Results further indicated that the validity of 3 of the Big Five traits--Neuroticism, Extraversion, and Conscientiousness--generalized across studies. As a set, the Big Five traits had an average multiple correlation of .49 with the motivational criteria, suggesting that the Big Five traits are an important source of performance motivation.

Personality and the prediction of consequential outcomes. <=SEP=> Personality has consequences. Measures of personality have contemporaneous and predictive relations to a variety of important outcomes. Using the Big Five factors as heuristics for organizing the research literature, numerous consequential relations are identified. Personality dispositions are associated with happiness, physical and psychological health, spirituality, and identity at an individual level; associated with the quality of relationships with peers, family, and romantic others at an interpersonal level; and associated with occupational choice, satisfaction, and performance, as well as community involvement, criminal activity, and political ideology at a social institutional level.

Personality and job performance: the Big Five revisited. <=SEP=> Prior meta-analyses investigating the relation between the Big 5 personality dimensions and job performance have all contained a threat to construct validity, in that much of the data included within these analyses was not derived from actual Big 5 measures. In addition, these reviews did not address the relations between the Big 5 and contextual performance. Therefore, the present study sought to provide a meta-analytic estimate of the criterion-related validity of explicit Big 5 measures for predicting job performance and contextual performance. The results for job performance closely paralleled 2 of the previous meta-analyses, whereas analyses with contextual performance showed more complex relations among the Big 5 and performance. A more critical interpretation of the Big 5-performance relationship is presented, and suggestions for future research aimed at enhancing the validity of personality predictors are provided.

Common method biases in behavioral research: a critical review of the literature and recommended remedies. <=SEP=> Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

The happy personality: a meta-analysis of 137 personality traits and subjective well-being. <=SEP=> This meta-analysis used 9 literature search strategies to examine 137 distinct personality constructs as correlates of subjective well-being (SWB). Personality was found to be equally predictive of life satisfaction, happiness, and positive affect, but significantly less predictive of negative affect. The traits most closely associated with SWB were repressive-defensiveness, trust, emotional stability, locus of control-chance, desire for control, hardiness, positive affectivity, private collective self-esteem, and tension. When personality traits were grouped according to the Big Five factors, Neuroticism was the strongest predictor of life satisfaction, happiness, and negative affect. Positive affect was predicted equally well by Extraversion and Agreeableness. The relative importance of personality for predicting SWB, how personality might influence SWB, and limitations of the present review are discussed.

Approximate 32-bit floating-point unit design with 53% power-area product reduction <=SEP=> The floating-point unit is one of the most common building block in any computing system and is used for a huge number of applications. By combining two state-of-the-art techniques of imprecise hardware, namely Gate-Level Pruning and Inexact Speculative Adder, and by introducing a novel Inexact Speculative Multiplier architecture, three different approximate FPUs and one reference IEEE-754 compliant FPU have been integrated in a 65 nm CMOS process within a low-power multi-core processor. Silicon measurements show up to 27% power, 36% area and 53 % power-area product savings compared to the IEEE-754 single-precision FPU. Accuracy loss has been evaluated with a high-dynamic-range image tone-mapping algorithm, resulting in small but non-visible errors with image PSNR value of 90 dB.

Meandered Variable Pitch angle Printed Quadrifilar Helix Antenna <=SEP=> A new method of Meandered Variable Pitch angle Printed Quadrifilar Helix Antenna (MVPQHA) is described. This type of antenna helps in reducing the size of the Printed Quadrifilar Helix Antenna (PQHA) and makes the bandwidth of the antenna broader. The new type of antenna is compared with other existing types of Printed Quadrifilar helix antenna in terms of size, return loss and bandwidth.

Action recognition based on a bag of 3D points <=SEP=> This paper presents a method to recognize human actions from sequences of depth maps. Specifically, we employ an action graph to model explicitly the dynamics of the actions and a bag of 3D points to characterize a set of salient postures that correspond to the nodes in the action graph. In addition, we propose a simple, but effective projection based sampling scheme to sample the bag of 3D points from the depth maps. Experimental results have shown that over 90% recognition accuracy were achieved by sampling only about 1% 3D points from the depth maps. Compared to the 2D silhouette based recognition, the recognition errors were halved. In addition, we demonstrate the potential of the bag of points posture model to deal with occlusions through simulation.

Need Satisfaction , Motivation , and Well-Being in the Work Organizations of a Former Eastern Bloc Country : A Cross-Cultural Study of Self-Determination <=SEP=> Past studies in U.S. work organizations have supported a model derived from self-determination theory in which autonomy-supportive work climates predict satisfaction of the intrinsic needs for competence, autonomy, and relatedness, which in turn predict task motivation and psychological adjustment on the job. To test this model cross-culturally, the authors studied employees of state-owned companies in Bulgaria, a country that has traditionally had a central-planning economy, a totalitarian political system, and collectivist values. A sample from a privately owned American corporation was used for comparison purposes. Results using structural equation modeling suggested that the model fit the data from each country, that the constructs were equivalent across countries, and that some paths of the structural model fit equivalently for the two countries but that county moderated the other paths.

WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices <=SEP=> We present a logarithmic-scale efficient convolutional neural network architecture for edge devices, named WaveletNet. Our model is based on the well-known depthwise convolution, and on two new layers, which we introduce in this work: a wavelet convolution and a depthwise fast wavelet transform. By breaking the symmetry in channel dimensions and applying a fast algorithm, WaveletNet shrinks the complexity of convolutional blocks by an O(logD/D) factor, where D is the number of channels. Experiments on CIFAR-10 and ImageNet classification show superior and comparable performances of WaveletNet compared to state-of-the-art models such as MobileNetV2.

Improving the Coverage and Spectral Efficiency of Millimeter-Wave Cellular Networks Using Device-to-Device Relays <=SEP=> The susceptibility of millimeter waveform propagation to blockages limits the coverage of millimeter-wave (mmWave) signals. To overcome blockages, we propose to leverage two-hop device-to-device (D2D) relaying. Using stochastic geometry, we derive expressions for the downlink coverage probability of relay-assisted mmWave cellular networks when the D2D links are implemented in either uplink mmWave or uplink microwave bands. We further investigate the spectral efficiency (SE) improvement in the cellular downlink, and the effect of D2D transmissions on the cellular uplink. For mmWave links, we derive the coverage probability using dominant interferer analysis while accounting for both blockages and beamforming gains. For microwave D2D links, we derive the coverage probability considering both line-of-sight and non-line-of-sight (NLOS) propagation. Numerical results show that downlink coverage and SE can be improved using two-hop D2D relaying. Specifically, microwave D2D relays achieve better coverage because D2D connections can be established under NLOS conditions. However, mmWave D2D relays achieve better coverage when the density of interferers is large because blockages eliminate interference from NLOS interferers. The SE on the downlink depends on the relay mode selection strategy, and mmWave D2D relays use a significantly smaller fraction of uplink resources than microwave D2D relays.

Ip = Pspace <=SEP=> In this paper, it is proven that when both randomization and interaction are allowed, the proofs that can be verified in polynomial time are exactly those proofs that can be generated with polynomial space.

Polyphase filters - A model for teaching the art of discovery in DSP <=SEP=> By its very nature DSP is a mathematically heavy topic and to fully understand it students need to understand the mathematical developments underlying DSP topics. However, relying solely on mathematical developments often clouds the true nature of the foundation of a result. It is likely that students who master the mathematics may still not truly grasp the key ideas of a topic. Furthermore, teaching DSP topics by merely “going through the mathematics” deprives students of learning the art of discovery that will make them good researchers. This paper uses the topic of polyphase decimation and interpolation to illustrate how it is possible to maintain rigor yet teach using less mathematical approaches that show students how researchers think when developing new ideas.

Random Projection Based Representations for Learning Policies in Deterministic Atari Games <=SEP=> Recent advances in sample efficient reinforcement learning algorithms in quasi-deterministic environments highlight the requirement for computationally inexpensive visual representations. Here we investigate non-parametric dimensionality reduction techniques based on random linear transformations and we provide empirical evidence on the importance of high-variance projections using sparse random matrices in the context of episodic controllers learning deterministic policies. We also propose a novel Maximum-Variance Random Projection and improve on the performance of the original Model-Free Episodic Control results with respect to both sample efficiency and final average score.

Unsupervised Meta-Learning for Reinforcement Learning <=SEP=> Meta-learning is a powerful tool that builds on multi-task learning to learn how to quickly adapt a model to new tasks. In the context of reinforcement learning, meta-learning algorithms can acquire reinforcement learning procedures to solve new problems more efficiently by meta-learning prior tasks. The performance of meta-learning algorithms critically depends on the tasks available for meta-training: in the same way that supervised learning algorithms generalize best to test points drawn from the same distribution as the training points, meta-learning methods generalize best to tasks from the same distribution as the meta-training tasks. In effect, meta-reinforcement learning offloads the design burden from algorithm design to task design. If we can automate the process of task design as well, we can devise a meta-learning algorithm that is truly automated. In this work, we take a step in this direction, proposing a family of unsupervised meta-learning algorithms for reinforcement learning. We describe a general recipe for unsupervised meta-reinforcement learning, and describe an effective instantiation of this approach based on a recently proposed unsupervised exploration technique and model-agnostic meta-learning. We also discuss practical and conceptual considerations for developing unsupervised meta-learning methods. Our experimental results demonstrate that unsupervised meta-reinforcement learning effectively acquires accelerated reinforcement learning procedures without the need for manual task design, significantly exceeds the performance of learning from scratch, and even matches performance of meta-learning methods that use hand-specified task distributions.

A Survey on Visual Approaches for Analyzing Scientific Literature and Patents <=SEP=> The increasingly large number of available writings describing technical and scientific progress, calls for advanced analytic tools for their efficient analysis. This is true for many application scenarios in science and industry and for different types of writings, comprising patents and scientific articles. Despite important differences between patents and scientific articles, both have a variety of common characteristics that lead to similar search and analysis tasks. However, the analysis and visualization of these documents is not a trivial task due to the complexity of the documents as well as the large number of possible relations between their multivariate attributes. In this survey, we review interactive analysis and visualization approaches of patents and scientific articles, ranging from exploration tools to sophisticated mining methods. In a bottom-up approach, we categorize them according to two aspects: (a) data type (text, citations, authors, metadata, and combinations thereof), and (b) task (finding and comparing single entities, seeking elementary relations, finding complex patterns, and in particular temporal patterns, and investigating connections between multiple behaviours). Finally, we identify challenges and research directions in this area that ask for future investigations.

Automatic number plate recognition system using modified stroke width transform <=SEP=> Automatic number plate recognition(ANPR) techniques occupy a significant role in intelligent traffic management systems. Most of the existing ANPR systems do not perform well under conditions like high vehicle speed, varying illuminations, changing back ground etc. In this paper we propose an ANPR technique which efficiently solves the above issues using the stroke width transform. In our method, first we pre-process the image to remove blurring artifacts. Then the significant edges in the image are detected and a new image is formed by grouping the connecting rays. This is followed by morphological processing which detects the letters in the image. Then an appropriate Optical Character Recognizer (OCR) recovers the final result. Experimental results show that the proposed method work effectively under varying light conditions and even in the presence of shadows.

Colour image segmentation using the self-organizing map and adaptive resonance theory <=SEP=> We propose a new competitive-learning neural network model for colour image segmentation. The model, which is based on the adaptive resonance theory (ART) of Carpenter and Grossberg and on the self-organizing map (SOM) of Kohonen, overcomes the limitations of (i) the stability–plasticity trade-offs in neural architectures that employ ART; and (ii) the lack of on-line learning property in the SOM. In order to explore the generation of a growing feature map using ART and to motivate the main contribution, we first present a preliminary experimental model, SOMART, based on Fuzzy ART. Then we propose the new model, SmART, that utilizes a novel lateral control of plasticity to resolve the stability–plasticity problem. SmART has been experimentally found to perform well in RGB colour space, and is believed to be more coherent than Fuzzy ART. q 2005 Elsevier Ltd. All rights reserved.

Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting <=SEP=> Traffic forecasting is a vital part of intelligent transportation systems. It becomes particularly challenging due to short-term (e.g., accidents, constructions) and long-term (e.g., peak-hour, seasonal, weather) traffic patterns. While most of the previously proposed techniques focus on normal condition forecasting, a single framework for extreme condition traffic forecasting does not exist. To address this need, we propose to take a deep learning approach. We build a deep neural network based on long short term memory (LSTM) units. We apply Deep LSTM to forecast peak-hour traffic and manage to identify unique characteristics of the traffic data. We further improve the model for postaccident forecasting with Mixture Deep LSTM model. It jointly models the normal condition traffic and the pattern of accidents. We evaluate our model on a realworld large-scale traffic dataset in Los Angeles. When trained end-to-end with suitable regularization, our approach achieves 30%-50% improvement over baselines. We also demonstrate a novel technique to interpret the model with signal stimulation. We note interesting observations from the trained neural network.

Weighted cluster ensembles: Methods and analysis <=SEP=> Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. Cluster ensembles can provide robust and stable solutions by leveraging the consensus across multiple clustering results, while averaging out emergent spurious structures that arise due to the various biases to which each participating algorithm is tuned. In this article, we address the problem of combining multiple weighted clusters that belong to different subspaces of the input space. We leverage the diversity of the input clusterings in order to generate a consensus partition that is superior to the participating ones. Since we are dealing with weighted clusters, our consensus functions make use of the weight vectors associated with the clusters. We demonstrate the effectiveness of our techniques by running experiments with several real datasets, including high-dimensional text data. Furthermore, we investigate in depth the issue of diversity and accuracy for our ensemble methods. Our analysis and experimental results show that the proposed techniques are capable of producing a partition that is as good as or better than the best individual clustering.

Local Probabilistic Models for Link Prediction <=SEP=> One of the core tasks in social network analysis is to predict the formation of links (i.e. various types of relationships) over time. Previous research has generally represented the social network in the form of a graph and has leveraged topological and semantic measures of similarity between two nodes to evaluate the probability of link formation. Here we introduce a novel local probabilistic graphical model method that can scale to large graphs to estimate the joint co-occurrence probability of two nodes. Such a probability measure captures information that is not captured by either topological measures or measures of semantic similarity, which are the dominant measures used for link prediction. We demonstrate the effectiveness of the co-occurrence probability feature by using it both in isolation and in combination with other topological and semantic features for predicting co-authorship collaborations on real datasets.

Effects of gratitude meditation on neural network functional connectivity and brain-heart coupling <=SEP=> A sense of gratitude is a powerful and positive experience that can promote a happier life, whereas resentment is associated with life dissatisfaction. To explore the effects of gratitude and resentment on mental well-being, we acquired functional magnetic resonance imaging and heart rate (HR) data before, during, and after the gratitude and resentment interventions. Functional connectivity (FC) analysis was conducted to identify the modulatory effects of gratitude on the default mode, emotion, and reward-motivation networks. The average HR was significantly lower during the gratitude intervention than during the resentment intervention. Temporostriatal FC showed a positive correlation with HR during the gratitude intervention, but not during the resentment intervention. Temporostriatal resting-state FC was significantly decreased after the gratitude intervention compared to the resentment intervention. After the gratitude intervention, resting-state FC of the amygdala with the right dorsomedial prefrontal cortex and left dorsal anterior cingulate cortex were positively correlated with anxiety scale and depression scale, respectively. Taken together, our findings shed light on the effect of gratitude meditation on an individual’s mental well-being, and indicate that it may be a means of improving both emotion regulation and self-motivation by modulating resting-state FC in emotion and motivation-related brain regions.

Printed Circuit Board Deconstruction Techniques <=SEP=> The primary purpose of printed circuit board (PCB) reverse engineering is to determine electronic system or subsystem functionality by analyzing how components are interconnected. We performed a series of experiments using both inexpensive home-based solutions and state-of-the-art technologies with a goal of removing exterior coatings and accessing individual PCB layers. This paper presents our results from the most effective techniques.

RIC: Relaxed Inclusion Caches for mitigating LLC side-channel attacks <=SEP=> Recently, side-channel attacks on Last Level Caches (LLCs) were demonstrated. The attacks require the ability to evict critical data from the cache hierarchy, making future accesses visible. We propose Relaxed Inclusion Caches (RIC), a low-complexity cache design protecting against LLC side channel attacks. RIC relaxes inclusion when it is not needed, preventing the attacker from replacing the victim's data from the local core caches thus protecting critical data from leakage. RIC improves performance (by about 10%) and retains snoop filtering capabilities of inclusive cache hierarchies, while requiring only minimal changes to the cache.

Curcumin: From ancient medicine to current clinical trials <=SEP=> Curcumin is the active ingredient in the traditional herbal remedy and dietary spice turmeric (Curcuma longa). Curcumin has a surprisingly wide range of beneficial properties, including anti-inflammatory, antioxidant, chemopreventive and chemotherapeutic activity. The pleiotropic activities of curcumin derive from its complex chemistry as well as its ability to influence multiple signaling pathways, including survival pathways such as those regulated by NF-κB, Akt, and growth factors; cytoprotective pathways dependent on Nrf2; and metastatic and angiogenic pathways. Curcumin is a free radical scavenger and hydrogen donor, and exhibits both pro- and antioxidant activity. It also binds metals, particularly iron and copper, and can function as an iron chelator. Curcumin is remarkably non-toxic and exhibits limited bioavailability. Curcumin exhibits great promise as a therapeutic agent, and is currently in human clinical trials for a variety of conditions, including multiple myeloma, pancreatic cancer, myelodysplastic syndromes, colon cancer, psoriasis and Alzheimer’s disease.

Interference-Aware Channel Assignment in Multi-Radio Wireless Mesh Networks <=SEP=> The capacity problem in wireless mesh networks can be alleviated by equipping the mesh routers with multiple radios tuned to non-overlapping channels. However, channel assignment presents a challenge because co-located wireless networks are likely to be tuned to the same channels. The resulting increase in interference can adversely affect performance. This paper presents an interference-aware channel assignment algorithm and protocol for multi-radio wireless mesh networks that address this interference problem. The proposed solution intelligently assigns channels to radios to minimize interference within the mesh network and between the mesh network and co-located wireless networks. It utilizes a novel interference estimation technique implemented at each mesh router. An extension to the conflict graph model, the multi-radio conflict graph, is used to model the interference between the routers. We demonstrate our solution’s practicality through the evaluation of a prototype implementation in a IEEE 802.11 testbed. We also report on an extensive evaluation via simulations. In a sample multi-radio scenario, our solution yields performance gains in excess of 40% compared to a static assignment of channels.

An Edge Based Smart Parking Solution Using Camera Networks and Deep Learning <=SEP=> The smart parking industry continues to evolve as an increasing number of cities struggle with traffic congestion and inadequate parking availability. For urban dwellers, few things are more irritating than anxiously searching for a parking space. Research results show that as much as 30% of traffic is caused by drivers driving around looking for parking spaces in congested city areas. There has been considerable activity among researchers to develop smart technologies that can help drivers find a parking spot with greater ease, not only reducing traffic congestion but also the subsequent air pollution. Many existing solutions deploy sensors in every parking spot to address the automatic parking spot detection problems. However, the device and deployment costs are very high, especially for some large and old parking structures. A wide variety of other technological innovations are beginning to enable more adaptable systems—including license plate number detection, smart parking meter, and vision-based parking spot detection. In this paper, we propose to design a more adaptable and affordable smart parking system via distributed cameras, edge computing, data analytics, and advanced deep learning algorithms. Specifically, we deploy cameras with zoom-lens and motorized head to capture license plate numbers by tracking the vehicles when they enter or leave the parking lot; cameras with wide angle fish-eye lens will monitor the large parking lot via our custom designed deep neural network. We further optimize the algorithm and enable the real-time deep learning inference in an edge device. Through the intelligent algorithm, we can significantly reduce the cost of existing systems, while achieving a more adaptable solution. For example, our system can automatically detect when a car enters the parking space, the location of the parking spot, and precisely charge the parking fee and associate this with the license plate number.

Role of Emotional Intelligence for Academic Achievement for Students <=SEP=> In the current competitive environment where students are expected to perform multi roles with efficiency and effectiveness, it is highly needed to develop their right attitude and emotional intelligence towards the unseen complexities of life and quality education. As emotional intelligence is a subset of social intelligence with the ability to understand and monitor one’s own feelings and others too which allows a student to mine the required data for his academic achievement which is an outcome of education and the extent at which the educational goal has been achieved. The emphasis of this paper was to determine the factors which are affecting the development of emotional intelligence and its role in academic achievement for students. In this research secondary data has been collected out of which we find out the correlation between emotional intelligence and academic achievement and teaching emotional and social skills at school not only positively influence academic achievement during the year when these were taught but also leaves the impact in long term achievement. Findings of this paper present that academic achievement without emotional intelligence does not indicate future success and absence of emotional intelligence also indicate the week personality and ability to build relations at working place as well in schools and it is highly important for quality education.

Adaptive Clock Gating Technique for Low Power IP Core in SoC Design <=SEP=> Clock gating is a well-known technique to reduce chip dynamic power. This paper analyzes the disadvantages of some recent clock gating techniques and points out that they are difficult in system-on-chip (SoC) design. Based on the analysis of the intellectual property (IP) core model, an adaptive clock gating (ACG) technique which can be easily realized is introduced for the low power IP core design. ACG can automatically enable or disable the IP clock to reduce not only dynamic power but also leakage power with power gating technique. The experimental results on some IP cores in a real SoC show an average of 62.2% dynamic power reduction and 70.9% leakage power reduction without virtually performance impact.

Sustaining iterative game playing processes in DGBL: The relationship between motivational processing and outcome processing <=SEP=> Digital game-based learning (DGBL) has become a viable instructional option in recent years due to its support of learning motivation. Recent studies have mostly focused on identifying motivational factors in digital games (e.g., curiosity, rules, control) that support intrinsic motivation. These findings, however, are limited in two fronts. First, they did not depict the interactive nature of the motivational processing in DGBL. Second, they excluded the outcome processing (learners’ final effort versus performance evaluation) as a possible motivation component to sustain the iterative game playing cycle. To address these problems, situated in the integrative theory of Motivation, Volition, and Performance (MVP), this study examined the relationship between motivational processing and outcome processing in an online instructional game. The study surveyed 264 undergraduate students after playing the Trade Ruler online game. Based on the data collected by ARCS-based Instructional Materials Motivational Survey (IMMS), a regression analysis revealed a significant model between motivational processing (attention, relevance, and confidence) and the outcome processing (satisfaction). The finding preliminarily suggests that both motivational processing and outcome processing need to be considered when designing DGBL. Furthermore, the finding implies a potential relationship between intrinsic motives and extrinsic rewards in DGBL. Published by Elsevier Ltd.