Configuration control of seven-degree-of-freedom arms

A seven degree of freedom robot arm with a six degree of freedom end effector is controlled by a processor employing a 6 by 7 Jacobian matrix for defining location and orientation of the end effector in terms of the rotation angles of the joints, a 1 (or more) by 7 Jacobian matrix for defining 1 (or more) user specified kinematic functions constraining location or movement of selected portions of the arm in terms of the joint angles, the processor combining the two Jacobian matrices to produce an augmented 7 (or more) by 7 Jacobian matrix, the processor effecting control by computing in accordance with forward kinematics from the augmented 7 by 7 Jacobian matrix and from the seven joint angles of the arm a set of seven desired joint angles for transmittal to the joint servo loops of the arm. One of the kinematic functions constraints the orientation of the elbow plane of the arm. Another one of the kinematic functions minimizes a sum of gravitational torques on the joints. Still another kinematic function constrains the location of the arm to perform collision avoidance. Generically, one kinematic function minimizes a sum of selected mechanical parameters of at least some of the joints associated with weighting coefficients which may be changed during arm movement. The mechanical parameters may be velocity errors or gravity torques associated with individual joints

Know abnormal, find evil : frequent pattern mining for ransomware threat hunting and intelligence

Emergence of crypto-ransomware has significantly changed the cyber threat landscape. A crypto ransomware removes data custodian access by encrypting valuable data on victims’ computers and requests a ransom payment to reinstantiate custodian access by decrypting data. Timely detection of ransomware very much depends on how quickly and accurately system logs can be mined to hunt abnormalities and stop the evil. In this paper we first setup an environment to collect activity logs of 517 Locky ransomware samples, 535 Cerber ransomware samples and 572 samples of TeslaCrypt ransomware. We utilize Sequential Pattern Mining to find Maximal Frequent Patterns (MFP) of activities within different ransomware families as candidate features for classification using J48, Random Forest, Bagging and MLP algorithms. We could achieve 99% accuracy in detecting ransomware instances from goodware samples and 96.5% accuracy in detecting family of a given ransomware sample. Our results indicate usefulness and practicality of applying pattern mining techniques in detection of good features for ransomware hunting. Moreover, we showed existence of distinctive frequent patterns within different ransomware families which can be used for identification of a ransomware sample family for building intelligence about threat actors and threat profile of a given target

Deep dive into ransomware threat hunting and intelligence at fog layer

Ransomware, a malware designed to encrypt data for ransom payments, is a potential threat to fog layer nodes as such nodes typically contain considerably amount of sensitive data. The capability to efficiently hunt abnormalities relating to ransomware activities is crucial in the timely detection of ransomware. In this paper, we present our Deep Ransomware Threat Hunting and Intelligence System (DRTHIS) to distinguish ransomware from goodware and identify their families. Specifically, DRTHIS utilizes Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), two deep learning techniques, for classification using the softmax algorithm. We then use 220 Locky, 220 Cerber and 220 TeslaCrypt ransomware samples, and 219 goodware samples, to train DRTHIS. In our evaluations, DRTHIS achieves an F-measure of 99.6% with a true positive rate of 97.2% in the classification of ransomware instances. Additionally, we demonstrate that DRTHIS is capable of detecting previously unseen ransomware samples from new ransomware families in a timely and accurate manner using ransomware from the CryptoWall, TorrentLocker and Sage families. The findings show that 99% of CryptoWall samples, 75% of TorrentLocker samples and 92% of Sage samples are correctly classified

Commercialization of JPL Virtual Reality calibration and redundant manipulator control technologies

Within NASA's recent thrust for industrial collaboration, JPL (Jet Propulsion Laboratory) has recently established two technology cooperation agreements in the robotics area: one on virtual reality (VR) calibration with Deneb Robotics, Inc., and the other on redundant manipulator control with Robotics Research Corporation (RRC). These technology transfer cooperation tasks will enable both Deneb and RRC to commercialize enhanced versions of their products that will greatly benefit both space and terrestrial telerobotic applications

A simple contagion process describes spreading of traffic jams in urban networks

The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two novel macroscopic characteristics of network traffic, namely congestion propagation rate \b{eta} and congestion dissipation rate {\mu}. We describe the dynamics of congestion propagation and dissipation using these new parameters, \b{eta}, and {\mu}, embedded within a system of ordinary differential equations, analogous to the well-known Susceptible-Infected-Recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.Comment: 10 pages, 8 figure

Multiple sclerosis course and clinical outcomes in patients with comorbid asthma: a survey study

Objective: To determine if comorbid asthma is associated with accumulation of multiple sclerosis (MS)-related impairment and disability.Method: We sent a comprehensive questionnaire to a cohort of patients with MS and examined the association between comorbid asthma and reaching Expanded Disability Status Scale (EDSS) scores 4.0 and 6.0. Multiple Sclerosis Impact Scale (MSIS-29) scores were compared between patients with MS with and without comorbid asthma.Results: 680 patients participated in our study of whom 88 (12.9%) had comorbid asthma. There was no difference in the prevalence of asthma between our MS cohort and the England general population (OR: 0.89, 95% CI 0.68 to 1.17). We did not observe a significant association between having asthma and the risk of reaching EDSS scores 4.0 and 6.0 (HR: 1.29, 95% CI 0.93 to 1.77, and HR: 1.33, 95% CI 0.93 to 1.89, respectively) after controlling for confounders. Patients with MS with asthma reported higher level of psychological impairments (coefficient: 2.29, 95% CI 0.1 to 4.49).Conclusions: Asthma is a prevalent condition among patients with MS and it may contribute to the psychological impairment in MS. Although we did not observe significant association between comorbid asthma and physical disability in MS, it seems that the two conditions influence one another

Evaluation of the effects of physical and chemical factors on shrimp white spot syndrome disease

Decline in capture fisheries and sea food demand lead to improve shrimp aquaculture activities. Iran had good experiences on shrimp farming during tow decades. White spot disease collapse the shrimp farming activities in recent years. Although goater were the main site for shrimp culture but this site were affected by white spot disease (WSD). Environmental stressors were the main criteria for attention in this regard. An investigation was carried out to monitor management practices and to find out whether there is any relationship with occurrence of white spot disease and environmental parameters. Five semi-intensive shrimp farms were selected in bahookalat chabahar area (2500 ha). The farms were situated at goater area. Tree ponds from each farm at random were selected for the study. All major environmental parameters such as O2, temperature, salinity. PH, nitrogen were recognized by standard method. Logistic regression were used for relationship of water parameters with occurrence of white spot disease. There were no significant relationship between PH, salinity and nitrogen in ponds and canals. But significant variations were recorded for oxygen (1.58) Temperature (0.89) with occurrence of white spot disease. Pond aeration can use for reduction and prevention of diseases