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Optimization by natural selection Evolutionary algorithms are a heuristic-based approach to solving problems that cannot be easily solved in polynomial time, such as classically NP-Hard problems, and anything else that would take far too long to exhaustively process. When used on their own, they are typically applied to combinatorial problems; however, genetic algorithms are often used in tandem with other methods, acting as a quick way to find a somewhat optimal starting place for another algorithm to work off of. The premise of an evolutionary algorithm (to be further known as an EA) is quite simple given that you are familiar with the process of natural selection. An EA contains four overall steps: initialization, selection, genetic operators, and termination. These steps each correspond, roughly, to a particular facet of natural selection, and provide easy ways to modularize implementations of this algorithm category. Simply put, in an EA, fitter members will survive and proliferate, while unfit members will die off and not contribute to the gene pool of further generations, much like in natural selection. Context In the scope of this article, we will generally define the problem as such: we wish to find the best combination of elements that maximizes some fitness function, and we will accept a final solution once we have either ran the algorithm for some maximum number of iterations, or we have reached some fitness threshold. This scenario is clearly not the only way to use an EA, but it does encompass many common applications in the discrete case. Initialization In order to begin our algorithm, we must first create an initial population of solutions. The population will contain an arbitrary number of possible solutions to the problem, oftentimes called members. It will often be created randomly (within the constraints of the problem) or, if some prior knowledge of the task is known, roughly centered around what is believed to be ideal. It is important that the population encompasses a wide range of solutions, because it essentially represents a gene pool; ergo, if we wish to explore many different possibilities over the course of the algorithm, we should aim to have many different genes present. Selection Once a population is created, members of the population must now be evaluated according to a fitness function. A fitness function is a function that takes in the characteristics of a member, and outputs a numerical representation of how viable of a solution it is. Creating the fitness function can often be very difficult, and it is important to find a good function that accurately represents the data; it is very problem-specific. Now, we calculate the fitness of all members, and select a portion of the top-scoring members. Multiple objective functions EAs can also be extended to use multiple fitness functions. This complicates the process somewhat, because instead of being able to identify a single optimal point, we instead end up with a set of optimal points when using multiple fitness functions. The set of optimal solutions is called the Pareto frontier, and contains elements that are equally optimal in the sense that no solution dominates any other solution in the frontier. A decider is then used to narrow the set down a single solution, based on the context of the problem or some other metric. Genetic Operators This step really includes two sub-steps: crossover and mutation. After selecting the top members (typically top 2, but this number can vary), these members are now used to create the next generation in the algorithm. Using the characteristics of the selected parents, new children are created that are a mixture of the parents’ qualities. Doing this can often be difficult depending on the type of data, but typically in combinatorial problems, it is possible to mix combinations and output valid combinations from these inputs. Now, we must introduce new genetic material into the generation. If we do not do this crucial step, we will become stuck in local extrema very quickly, and will not obtain optimal results. This step is mutation, and we do this, quite simply, by changing a small portion of the children such that they no longer perfectly mirror subsets of the parents’ genes. Mutation typically occurs probabilistically, in that the chance of a child receiving a mutation as well as the severity of the mutation are governed by a probability distribution. Termination Eventually, the algorithm must end. There are two cases in which this usually occurs: either the algorithm has reached some maximum runtime, or the algorithm has reached some threshold of performance. At this point a final solution is selected and returned. Example Now, just to illustrate the result of this process I will show an example of an EA in action. The following gif shows several generations of dinosaurs learning to walk by optimizing their body structure and applied muscular forces. From left to right the generation increases, so the further right, the more optimized the walking process is. Despite the fact that the early generation dinosaurs were unable to walk, the EA was able to evolve the dinosaurs over time through mutation and crossover into a form that was able to walk. Sursa: https://towardsdatascience.com/introduction-to-evolutionary-algorithms-a8594b484ac

Two U.S. senators recently proposed a cybersecurity legislation that will allow the Federal Trade Commission (FTC) to penalize credit rating industry organizations that don’t properly safeguard data. Cybersecurity Legislation Imposes Penalties for Breaches In a public statement outlining the proposed Data Breach Prevention and Compensation Act, Sens. Elizabeth Warren (D-Mass.) and Mark Warner (D-Va.) explained that the bill would create a new office at the FTC focused on information protection. If passed, it would enact strict penalties for breaches in customer data. Specifically, credit rating agencies would receive $100 fines for each piece of personally identifiable information (PII) lost in a data breach, plus $50 for each additional PII file per customer. According to SecurityWeek, the bill also requires agencies that fail to comply to pay a maximum penalty of 50 percent their gross revenue from the year before the incident took place. In addition to giving the FTC greater oversight and power over data protection practices, this cybersecurity legislation actually hits harder in terms of fines than the EU’s General Data Protection Regulation (GDPR). While many firms are bracing for GDPR to come into effect later this year, it’s clear that recent security headlines are creating just as much concern among lawmakers on this side of the Atlantic. Protecting Consumer Data The bill aims to ensure that consumers, whose personal information becomes the ultimate casualty when cybercriminals break into large corporate systems, will be fairly compensated: 50 percent of the fines collected by the FTC would go to the victims. The other half would go toward security research and inspections, SecurityWeek noted, ensuring that the law would also reduce the risk of similar occurrences in the future. It’s not unusual for modern governments to consider cybersecurity legislation. Just as credit agencies keep a close eye on how consumers spend their money, the government wants to keep an even closer eye on how these firms are keeping data from prying eyes. https://securityintelligence.com/news/new-cybersecurity-legislation-to-penalize-companies-for-data-breaches/

Pai app design, app dev (adica UI/UX designer, Android/iOS devs, back-end devs, teste calitate). Depinde de nr. de oameni care lucreaza la proiect. Cel putin 4 luni. Intre 40 si 100 de coco pe ora. In cel mai bun caz scapi cu 5k eur, eu as cere mult, mult mai mult. Acum poate gasesti niste murichiciori de foame care sa-ti faca o aplicatie plina de bug-uri care mai crash-eaza din cand in cand pe 5k . Asta e proiect de cateva zeci de mii de coco. Imi place totusi ca la voi filmele ruleaza continuu, mai ceva ca la cinema. Tu in ce film joci? -secure auth; auth via Facebook/others -user profiles (editing, etc) -app settings -geolocation -matching algo -chat -push notifications -sms Ah si-ti trebuie suport in permanenta (dezvoltare, bug fixing), pe cineva bun cu bazele de date. Intrebarea a fost pusa in sila (adica nu suntem sclavii tai) si ar fi trebuit sa te iau la pla in loc sa-ti raspund, dar sunt in toane mai bune in seara asta.

E ca si cum ai juca barbut si vor sa le dai impozit din ei. Banii aia pe care-i iei, au fost deja impozitati. Ei la fiecare mana prin care trece banul, vor impozit. Da-le la muie, nu au ce sa-ti faca.

salut, -si eu folosesc walletul de mymonero - pt comisioane mai mici iti sugerez cryptopia (adica din mymonero trimiti in cryptopia si de acolo convertesti la btc/usdt/nzdt/ltc/dodge si cu astea poti converti la alte monede) - pt monero iti recomand acest pool datorita comisionului mic, a puterii de minare destul de mare (nu astepti cu zilele pt rewardul tau) si ai si low payout: https://monero.hashvault.pro/en/#!/ -daca te-ai orientat catre cryptonight (monero) iti sugerez sa minezi alte monede precum ETN sau ISTN (recomand ETN), poti mina ETN pe poolul acesta(este cel mai bun si mai serios pool dintre toate pe care le-am testat): https://etn.spacepools.org/# -un exchange fara comisiona este cobinhood dar momentan are doar cateva monede (pune-l pe wachlist) - pentru ETN ca sa-ti faci o idee: Nota bene: walletul de ETN il faci din paper wallet(generare initiala)+ download https://github.com/electroneum/electroneum/releases (trebuie sa downloadezi tot blockchainul pt ETN - o sa dureze ceva) + import paper wallet ------gasesti pe youtube tutorial daca nu te descurci pentru cryptonight in general cand minezi cu cpu trebuie sa ai in vedere urmatorul aspect foarte important: - numarul de threaduri = Cache procesor impartit la 2 *daca ai 24 threaduri la procesor si doar 12Mb Cache atunci vei folosi doar 6 threaduri (12/2=6) >> cate 2Mb per thread. **recomand xmr-stak (linux/windows compilat din sursa si scos comisionul de developer > donate-level.h) spor

SKRILL + card data viitoare nu declari nimic acum la anaf, veziti de treaba. (cunosc persoane care extrag 6000$ lunar din paypal pe contul bancar...de cativa ani incoa---nu i-a deranjat nici un anaf).

Problem solved: http://aksitha.com/VideoTraining/Video%20Training%20Database/SQL/SQL%20Injection%20Master%20Training%20Course%202014/

Ce vremuri s-o ajuns. Inainte cautai sa trimiti sms gratis, acum cauti sa primesti

Cum "rezolv" si eu urmatoarea problema https://hack.me/101052/vulnerable-webapp.html Comentariile din link nu prea ma ajuta fiind mai incepator de felul meu.

Sunt sanse, mici dar sunt, si ori ti se da amenda ori daca sumele sunt f mari am impresia ca intri la evaziune fiscala si posibil puscarie

Aplicatia o mai scoti cu mai putin de 5k, dar mentenanta si publicitatea costa milioane.