Income Verification Matrix: Fill & Download for Free

There are several major closed-form challenges in Computer Science that are prime targets for research, such asthe P versus NP problem,finding better algorithms for Matrix multiplication and Fourier transform,building Quantum computers that can quickly factorize numbers into primes (Shor's algorithm), or at least explaining why we are still so far from this goal.However, the hottest topics are broad and intentionally defined with some vagueness, to encourage out-of-the-box thinking. For such topics, zooming in on the right questions often marks significant progress in itself.Here's my list for 2015.Abundant-data applications, algorithms and architectures are a meta-topic that includes research avenues such as data mining (quickly finding relatively simple patterns in massive amounts of loosely structured data, evaluating and labeling data, etc), machine learning (building mathematical models that represent structure and statistical trends in data, with good predictive properties), hardware architectures to process more data than is possible today.Artificial intelligence and robotics - broadly, figuring out how to formalize human capabilities which currently appear beyond the reach of computers and robots, then make computers and robots more efficient at it. Self-driving cars and swarms of search-and-rescue robots are a good illustration. In the past, once good model were found for something (such as computer-aided design of electronic circuits), this research moves into a different field - the design of efficient algorithms, statistical models, computing hardware, etc.Bio-informatics and other uses of CS in biology, biomedical engineering and medicine, including systems biology (modeling interactions of multiple systems in a living organism, including immune systems and cancer development), computational biophysics (modeling and understanding mechanical, electrical, and molecular-level interactions inside an organism), computational neurobiology (understanding how organisms process incoming information and react to it, control their bodies, store information, and think). There is a very large gap between what is known about brain structure and the functional capabilities of a living brain - closing this gap is one of the grand challenged in modern science and engineering. DNA analysis and genetics have also become computer-based in the last 20 years. Biomedical engineering is another major area of growth, where microprocessor-based systems can monitor vital signs and even administer life-saving medications without waiting for a doctor. Computer-aided design of prosthetics is very promising.Computer-assisted education, especially at the high-school level. Even for CS, few high schools offer competent curriculum, even in developped countries. Cheat-proof automated support for exams and testing, essay grading, generation of multiple-choice questions. Support for learning specific skills, such as programming (immediate feedback on simple mistakes and suggestions on how to fix them, peer grading, style analysis).Databases, data centers, information retrieval and natural-language processing: collecting and storing massive collections of data and making them easily available (indexing, search), helping computers understand (structure in) human-generated documents and artifacts of all kinds (speech, video, text, motion, biometrics) and helping people search for the information they need when they need it. There are many interactions with abundant-data applications here, as well as with human-computer interaction, as well as with networking.Emerging technologies for computing hardware, communication and sensing: new models of computation (such as optical and quantum computing) and figuring out what they are [not] good for. Best uses for three-dimensional integrated circuits and a variety of new memory chips. Modeling and using new types of electronic switches (memristors, devices using carbon nano-tubes, etc), quantum communication and cryptography, and a lot more.Human-computer interaction covers human-computer interface design and focused techniques that allow computers to understand people (detect emotions, intent, level of skill), as well as the design of human-facing software (social networks) and hardware (talking smart-phones and self-driving cars).Large-scale networking: high-performance hardware for data centers, mobile networking, support for more efficient multicast, multimedia, and high-level user-facing services (social networks), networking services for developing countries (without permanent high-bandwidth connections), various policy issues (who should run the Internet and whether the governments should control it). Outer-space communication networks. Network security (which I also listed under Security) is also a big deal.Limits of computation and communication at the level of problem types (some problems cannot be solved in principle!), algorithms (sometimes an efficient algorithm is unlikely to exist) and physical resources, especially space, time, energy and materials. This topic covers Complexity Theory from Theoretical CS, but also the practical obstacles faced by the designers of modern electronic systems, hinting at limits that have not yet been formalized.Multimedia: graphics, audio (speech, music, ambient sound), video - analysis, compression, generation, playback, multi-channel communication etc. Both hardware and software are involved. Specific questions include scene analysis (describing what's on the picture), comprehending movement, synthesizing realistic multimedia, etc.Programming languages and environments: automated analysis of programs in terms of correctness and resource requirements, comparisons between languages, software support for languages (i.e., compilation), program optimization, support for parallel programming, domain-specific languages, interactions between languages, systems that assist programmers by inferring their intent.Security of computer systems and support for digital democracy, including network-level security (intrusion detection and defense), OS-level security (anti-virus SW) and physical security (biometrics, tamper-proof packaging, trusted computing on untrusted platforms), support for personal privacy (efficient and user-friendly encryption), free speech (file sharing, circumventing sensors and network restrictions by oppressive regimes), as well as issues related to electronic polls and voting. Security is also a major issue in the use of embedded systems and the Internet of Things (IoT).Verification, proofs, and automated debugging of hardware designs, software, networking protocols, mathematical theorems, etc. This includes formal reasoning (proof systems and new types of logical arguments), finding bugs efficiently and diagnosing them, finding bug fixes, and confirming the absence of bugs (usually by means of automated theorem-proving).If something is not listed, it may still be an interesting and worthwhile topic, but not necessarily "hot" right now, or perhaps lurking in my blind spot.Now that you have a long answer, let's revisit the question! Hotness usually refers to how easy it is to make impact in the field and how impactful the field is likely to be in the broader sense. For example, solving P vs. NP would be impactful and outright awesome, but also extremely unlikely to happen any time soon. So, new researchers are advised to stay away from such an established challenge. Quantum computing is roughly in the same category, although apparently the media and the masses have not realized this. On the positive side, applied physicists are building interesting new devices, producing results that are worthwhile by themselves. So, quantum information processing is a hot area in applied physics, but not in computer design.

The verification theory or principle is part of the positivist turn of 20th century philosophy. Ludwig Wittgenstein’s Tractatus (published in 1922) played an important role here.(British) empiricists and German idealists had held two rivaling positions (in a dilemma which Marxist materialists tried to solve with a dogma that was not satisfying for philosophers).The empiricist idea is that you see things and they produce a picture in your head (same goes for all other forms of sensual data: something comes from outside and you get the note of the event into your brain and turn that into knowledge). If you want to act as an empiricist you only deal with sensations, not with any imaginary ideas (that have to be left to Metaphysical thinkers).Idealists such as Kant said that this was gross, because you will (in this case) never ever deal with anything outside. You actually do not have a clue of the real outside world (the “Ding an sich”, the thing as it really is, as Kant called it). All you have is sensations and, when it comes to the outside world: “ideas” of what caused these sensations.Materialists intervened in this dilemma because all the idealist and Kantian talk of ideas was only made to support metaphysics and religion, so their observation. God was for idealists just as real as horses and houses because everything had to be reduced to ideas. The Marxist view was dogmatic: There is real matter out there and that is causing the sensation. You cannot prove it (because how could you prove it is not a dream?) but you have to state it dogmatically or you end up with a philosophy without substance and without real power to change things.The positivist turn was the turn towards the scientific statement in all its forms from satellite image to carbon dating. You make statements like those that produce maps on your car’s GPS. Statements like “there is a street that goes through coordinates AB … till XY. When using the map you can verify all these statements - there really is a road under your car, as said on the screen.You realise that you are actually constantly using the verification principle when suddenly you enter a zone where the data are apparently incorrect.You can with the verification principle forget questions like “Is there an outside world that really exists?” “Is that world only in your mind or just as much outside?”, “Is the real road substance of nothing but matrix projected into your mind by another regime?”All these questions are brushed aside by the realisation that you are dealing with statements which you can either (empirically) verify or falsify. The statement comes with an idea of what the verification would be - and only then it begins to make sense. In a way this theory is idealist. The entire GPS information and all maps are idealist stuff: a map offers ideas of things that might be so or that might just be fantasy (as on the map of Robinson Crusoe’s island). You are using this information and usually you realise: it works. That is the reality check of empirical observation put on statements - and tat is how empirism is now essential to the world of statements.The theory is anti-metaphysical because now you will see that statements about God are not part of the same verification processes you can use as a scientist in order to work with data.Note aside: Karl Popper tried to brush aside the verification principle by stating that we have only scientific progress where we falsify an observation. That is nice with some spectacular examples like the “discovery” of the Americas that forced us to rethink everything. It is otherwise wrong. Google creates Google maps not on the basis of falsifications. They collect positive data; and they do not offer their next version on the basis of all the incoming error reports - they take fresh data from satellites to create new positive images of streets.Note also that what is in your head is quite irrelevant here. You can use machines to create maps from satellite information and you can offer these maps to machines like those guiding a nuclear missile. They will do their jobs on the basis of information that has never entered a human brain in the process.Note2: What you assume to be a verification of a statement can of course be at odds with things - that again is your problem. The verification does what it does - you might be the one who misunderstands what you are actually checking here.

Manufacturing set-up is the most interesting one. Your question seems open-ended since you have not mentioned which function is to be audited. Whether its planning & ordering, sourcing,production, quality assurance,sales & marketing, after-market, inventory, scrapping, logistics etc.For example, if its inventory, one has to be clear on how the quantity to be stored is arrived at i.e. EOQ. Check slow-moving items, ageing, reason for very old items in stock, order-log, frequency of physical verification (while doing sample physical verification yourself, make sure to carry out reverse verification also i.e. first count physical stock and then tally back from system stock….you’ll find anomalies here and auditees will be taken by surprise), proper and traceable tags, treatment of defective items, quarantine, scrapping procedure(again a very tricky area…money laundering happens easily especially in cases where parts of an item do not get entered in the system separately and they have to be scrapped) etc.Usually, manufacturing companies do have SOPs for all processes because its very difficult to have a control over various activities in their absence. What one must check is their adherence by factory workers/employees. To ensure this, check authorization matrix. Trace incoming register at factory gate, who received the material, its entry in system, where it got stored, its issue to production line, its mention in BoM of finished goods.All the best with auditing. Just keep linkage of items in mind from the beginning till the end. And keep your eyes open.