TRADE UNIONS AND BASIC INCOME: SOME DANISH EXPERIENCES

Theoretically, there are many good arguments that unions should support a proposal on basic income. The main reason for the Danish trade unions resistance to basic income reform is that it would go against the short-term interest of the unions in organisational self-maintenance. Trade unions will lose power in relation to their members with a basic income. Trade unions have control over individual members by virtue of the collective agreement system and the labour law system. If you have a basic income system, the individual worker will decide when he or she wants to leave his workplace and strike. Suppose a single worker or a group of workers leave their workplace because of dissatisfaction with the working condition. In that case, they will be punished financially according to the rules of labour law rely on any support from their trade union.

Quantized Adam with Error Feedback

In this article, we present a distributed variant of an adaptive stochastic gradient method for training deep neural networks in the parameter-server model. To reduce the communication cost among the workers and server, we incorporate two types of quantization schemes, i.e., gradient quantization and weight quantization, into the proposed distributed Adam. In addition, to reduce the bias introduced by quantization operations, we propose an error-feedback technique to compensate for the quantized gradient. Theoretically, in the stochastic nonconvex setting, we show that the distributed adaptive gradient method with gradient quantization and error feedback converges to the first-order stationary point, and that the distributed adaptive gradient method with weight quantization and error feedback converges to the point related to the quantized level under both the single-worker and multi-worker modes. Last, we apply the proposed distributed adaptive gradient methods to train deep neural networks. Experimental results demonstrate the efficacy of our methods.

Advantages of arbitration over court resolution of employment disputes

An employment dispute is a dispute between a single worker and the employer, or between a trade union and employer or association of employers. The division of employment disputes is important because it indicates the method of the settlement of labour disputes. According to Serbian legal system, employment disputes can be settled in court or using one of the alternative labour dispute resolution methods (arbitration, conciliation, mediation). Based on the Law on the Amicable Settlement of Employment Disputes, an institution for the peaceful settlement of labour disputes was established - State Agency for Amicable Settlement of Employment Disputes. Arbitration is always voluntary. The Agency is to be in charge only if both parties accept to solve the dispute using arbitration. An individual employment dispute can be solved using arbitration only if that is allowed by work contract or a general employer act. A collective dispute can also be solved using arbitration. If one of the parties does not want to use an alternative method for solving the problem, arbitration cannot be an option. In that case, the only way to protect its rights is in court. The process of settling a dispute in court before a judge is an extremely strict and formal procedure required by law. The court is obligated to determine relevant facts and make a decision based on them. The judgment is binding for the parties and can be forcedly executed. Protection of rights is accomplished when the judgment or arbitration decision is made and executed. The arbitration decision can be voluntarily executed, unlike the judgment that can be forcedly executed. The arbitration decision cannot be forcedly executed and that is the main disadvantage of the arbitration. The arbitration procedure needs to be improved in order to guarantee the protection of rights.

A Deep Learning-Based Model for the Automated Assessment of the Activity of a Single Worker

Nowadays, it is necessary to verify the accuracy of servicing work, undertaken by new employees, within a manufacturing company. A gap in the research has been observed in effective methods to automatically evaluate the work of a newly employed worker. The main purpose of the study is to build a new, deep learning model, in order to automatically assess the activity of the single worker. The proposed approach integrates the methods known as CNN, CNN + SVM, CNN + R-CNN, four new algorithms and a piece of work from a selected company, using this as an own-created dataset, in order to create a solution enabling assessment of the activity of single workers. Data were collected from an operational manufacturing cell without any guided or scripted work. The results reveal that the model developed is able to accurately detect the correctness of the work process. The model’s accuracy mostly exceeds current state-of-the-art methods for detecting work activities in manufacturing. The proposed two-stage approach, firstly, assigning the appropriate graphic instruction to a given employee’s activity using CNN and then using R-CNN to isolate the object from the reference frames, yields 94.01% and 73.15% accuracy of identification, respectively.

Efficacy of the InvictDetectTM Immunostrip® to Taxonomically Identify the Red Imported Fire Ant, Solenopsis invicta, Using A Single Worker Ant

The early detection and identification of the red imported fire ant Solenopsis invicta are crucial to intercepting and preventing it from becoming established in new areas. Unfortunately, the visual identification of fire ants to species is difficult and ant samples must often be couriered to an expert for positive identification, which can delay control interventions. A lateral flow immunoassay that provides a rapid and portable method for the identification of S. invicta ants was developed and commercialized, and it is available from Agdia, Inc. under the trade name InvictDetectTM. While the test was 100% accurate when using the recommended minimum sample of three ant workers, InvictDetectTM was field tested for the first time while using homogenates prepared from single S. invicta workers to determine the effectiveness of the method under these non-recommended conditions. Disregarding social form, the false negative rate was 25.5% for an initial single worker ant test and 10% after a repeat test was performed. The InvictDetectTM false negative response was independent of worker weight. Though InvictDetectTM requires a minimum of three worker ants, the test improves upon current identification methods because it can be conducted in the field, be completed in 10–30 min, and requires no special training or expertise.

Sistem Peramalan Kebutuhan Hidup Layak Minimum (Kapita/Bulan) Kota Bandung

The importance of inflation forecasting is used as a reference for estimating the Need for Living (KHL). If inflation can be predicted with high accuracy, it can certainly be used as the basis for government policy making in anticipating future economic activity. This study aims to produce a model of inflation data forecasting system, the forecasting results can be used as a reference for determining the Decent Living Needs (KHL) of a single worker in one month. The data used in forecasting is inflation data for January 2011 - December 2017 while the KHL data for the food and beverage category is obtained from the food price portal. The method used in this study is the SARIMA-SES hybrid method. In forecasting the inflation rate where the data is in the form of time series, the SARIMA-SES hybrid method can show more accurate forecasting results than using a single method. Based on the comparison of the overall forecasting model and by combining the SARIMA (1,0,1) (1,0,1) 12 and SES models with alpha 0,6 the smallest error value with MAD value 0,114, MSE 0,017 and 0,39% for MAPE. From these results, it was gathered that inflation forecasting in Bandung City using the SARIMA-SES hybrid method has a high accuracy value so that the results of the KHL value calculation with the forecasting inflation value approach the actual value. From these values can be used as a reference for the decision making of a single worker in fulfilling their life needs one month.

Optimal Task Partition with Delay Requirement in Mobile Crowdsourcing

Mobile crowdsourcing takes advantage of mobile devices such as smart phones and tablets to process data for a lot of applications (e.g., geotagging for mobile touring guiding monitoring and spectrum sensing). In this paper, we propose a mobile crowdsourcing paradigm to make a task requester exploit encountered mobile workers for high-quality results. Since a task may be too complex for a single worker, it is necessary for a task requester to divide a complex task into several parts so that a mobile worker can finish a part of the task easily. We describe the task crowdsourcing process and propose the worker arrival model and task model. Furthermore, the probability that all parts of the complicated task are executed by mobile workers is introduced to evaluate the result of task crowdsourcing. Based on these models, considering computing capacity and rewards for mobile workers, we formulate a task partition problem to maximize the introduced probability which is used to evaluate the result of task crowdsourcing. Then, using a Markov chain, a task partition policy is designed for the task requester to realize high-quality mobile crowdsourcing. With this task partition policy, the task requester is able to divide the complicated task into precise number of parts based on mobile workers’ arrival, and the probability that the total parts are executed by mobile workers is maximized. Also, the invalid number of task assignment attempts is analyzed accurately, which is helpful to evaluate the resource consumption of requesters due to probing potential workers. Simulations show that our task partition policy improves the results of task crowdsourcing.

Unfit for Work, Fit for Firearm or driving license - Is that Possible?

BACKGROUND: Psychiatric disorders are not compatible with carrying firearms or with driving a car. Persons with such disorders are often not employed and are persistent in demanding invalidity pensions, but some of them also insist on holding on to the mentioned licenses. In such cases, where persons are already in possession of firearms and driving licences, it never occurs to them, that they should surrender their permits back. AIM Pointing to the importance of OM controlling firearm/car driving licenses. CASE REPORTS: This paper discusses the problem of three cases that should be widely recognised as it is potentially life-threatening to other people. The first is the case of a war veteran in retirement with PTSD that had his application for firearms licence rejected by the authorities. The second is the case of a labourer who suffers from a depressive disorder, temporarily incapable of work. The third is the case of a war veteran, a chronic alcoholic with toxic epilepsy, who is applying for invalidity retirement but wants to keep his driving license. CONCLUSION: Occupational medicine assess every single worker by applying advanced methods and psycho tests that enable a thorough assessment of work capacity and fitness for carriage of firearms, driving as well as the assessment of psychiatric disorders, which are the most delicate to assess.

Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning

In distributed training of deep neural networks, parallel minibatch SGD is widely used to speed up the training process by using multiple workers. It uses multiple workers to sample local stochastic gradients in parallel, aggregates all gradients in a single server to obtain the average, and updates each worker’s local model using a SGD update with the averaged gradient. Ideally, parallel mini-batch SGD can achieve a linear speed-up of the training time (with respect to the number of workers) compared with SGD over a single worker. However, such linear scalability in practice is significantly limited by the growing demand for gradient communication as more workers are involved. Model averaging, which periodically averages individual models trained over parallel workers, is another common practice used for distributed training of deep neural networks since (Zinkevich et al. 2010) (McDonald, Hall, and Mann 2010). Compared with parallel mini-batch SGD, the communication overhead of model averaging is significantly reduced. Impressively, tremendous experimental works have verified that model averaging can still achieve a good speed-up of the training time as long as the averaging interval is carefully controlled. However, it remains a mystery in theory why such a simple heuristic works so well. This paper provides a thorough and rigorous theoretical study on why model averaging can work as well as parallel mini-batch SGD with significantly less communication overhead.

A FORMAÇÃO DO FUNDO SANTO DIAS NO CEDEM; A CONSTRUÇÃO DA MEMÓRIA DO MÁRTIR OPERÁRIO * THE FORMATION OF THE SANTO DIAS NO CEDEM FUND; CONSTRUCTION OF THE MEMORY OF THE OPERATOR MARITAL

Este presente artigo tem como objetivo analisar parte do processo de construção da memória do operário Santo Dias da Silva a partir da constituição do acervo Fundo Santo Dias localizado no Centro de Memória e Documentação da UNESP (CEDEM). O Fundo Santo Dias é um dos poucos acervos no Brasil dedicados a memória de um único operário. Esse acervo foi em parte formado por documentos cedidos pela família, amigos e companheiros militantes das pastorais sociais e sindicatos. Apesar da dimensão da memória do operário Santo Dias ser muito maior que o acervo e estar presente em outras manifestações mnemônicas, a institucionalização dessa documentação foi muito importante para o estudo e manutenção da memória dos movimentos sociais no Brasil.*This paper aims to analyze part of the worker’s memory construction process Santo Dias da Silva since the incorporation of the Fund Santo Dias collection located in the Center of Memory and Documentation of UNESP (CEDEM). The Santo Dias Fund is one of the fewest collections in Brazil dedicated to the memory of a single worker. This collection was in part formed by documents granted by the family, friends and militant companions of the social pastoralists and unions. Although the worker’s memory size Santo Dias is much larger than the collection and it is present in other mnemonic manifestations, the institutionalization of this documentation was very important for the study and maintenance of the memory of social movements in Brazil.