https://www.xda-developers.com/windows-11-users-fight-back-against-microsoft-local-accounts/
Sunday, March 30, 2025
Thursday, March 27, 2025
Wednesday, March 26, 2025
Tuesday, March 25, 2025
Monday, March 24, 2025
Sunday, March 23, 2025
Monday, March 17, 2025
Flaws in Smartphone Face-recognition Software [Tech News]
From British consumer magazine WHICH
The problem with face-recognition: apparently, with some smartphones, holding an ordinary photograph up to the phone can unlock it, if I understand correctly. I suppose this means that the person stealing your mobile phone from you knows you, as they would need to have the photo in the first place.
I have avoided activating the face-recognition functionality on my smartphone (which is a basic Samsung) because I was wondering what you do if it doesn't work: the software has not been updated, or the place where you are is too dark and the phone cannot 'see' you, and so on.
The Extract below. The article may be behind a pay wall.
____________________________
In 2023, we revealed that over 40% of phones we tested had face recognition security that could be easily fooled and successfully unlocked by a 2D printed photograph. These included popular handsets from Samsung, Motorola, Xiaomi and others. Unfortunately the issue isn't going away and we were surprised that the same issue affects two phones in the Samsung Galaxy S24 series, and all phones in the S25 series.
Fortunately, Samsung
offers a clear warning during the face recognition set up that tells users the
system can be fooled with a photograph. But to avoid this vulnerability, we recommend you don't enable the feature and use the
fingerprint sensor or a password/PIN instead. Long PINS are generally more
secure (six characters), and if you can set up a password, use a mixture
of different characters so it's harder to guess. We recommend setting up
protections on your apps that contain sensitive information too – this could
involve logging out when you're not using them, or setting up passwords or
fingerprint locks.
We approached Samsung for comment on our findings about face recognition on the Samsung Galaxy S24 and Galaxy S24+ in 2024. It said: 'We provide various levels of biometric authentication, with the highest level of authentication from the fingerprint reader. In addition, we provide users with multiple options to unlock their smartphones through both biometric security methods, and convenient options such as swipe or facial recognition. Further information about facial recognition can be found via the settings on Samsung Galaxy smartphones.'
Friday, March 14, 2025
Thursday, March 13, 2025
Monday, March 10, 2025
Conflict & Reconciliation Feature for Social Networks - A Proposal
Subject Matter & Problem
In the course of interactions among human beings, there arises, perhaps inadvertently and perhaps otherwise, that one or more parties take offense or are made angry by the utterances or behavior of another party or parties. Human societies, historically, have developed mechanism to cope with social friction among the members, limit its negative consequences, and to facilitate reconciliation among the affected parties.
Virtual communities and social networks such as Facebook,
Google Groups, etc. are novel ways of human interactions and group formation
which are based on computer and information technologies. However, they are not immune to the
occurrences of social friction among their members and the attendant issues of
anger management and reconciliation among “warring” parties. Facebook, for example, offers a binary choice
between a “Friend” and “Un-Friend”; one may “Un-Friend” a “Friend” if one has
been angered – or otherwise disappointed – by that person. There is no intermediate state between a
“Friend” and “Un-Friend” which could distinguish among total strangers in
contradistinction to former friends and acquaintances who could, when one’s
anger cools, become one’s friends again.
Solution
The gist of this solution is to endow the social networking
sites and virtual communities with a feature to mediate and affect a
reconciliation among former friends and acquaintances beyond the binary choice
of “Friend” and “Un-Friend” and to further facilitate the resolution of the
conflict through selected “Intercessors”.Use Case 1 – Normal Use
- The user navigates to a screen that contains a list of his contacts.
- He selects one or more of them and designates them as: “Not on Speaking Terms”, or “Sulking”, or “Furious” or some such phrase (or its equivalent in other languages). That is, he sets their “Conflict” status.
- The system updates their profiles accordingly.
- The system displays the text above and an appropriate icon that indicates that these individuals have been thus designated as being in “Conflict” with this or that user.
This will be visible to all.
This designation is not conceived to be
equivalent to “Un-Friend”.
- The user could further designate a time period for that status: from “Never Expires” to a date and time range (either selectable from a screen widget or entered manually by the user).
- The user may optionally decide if he wants to block communication from the “Conflict” contacts.
- The system will notify those contacts of a change in their status.
That communication could be in the form of
email, internal communication, text messages, phone calls etc. – together with
an appropriate text message.
- The system will notify others who share the affected contacts with the user and are also in the user’s social network of the “conflict” status change above.
- When the “Conflict” status time interval expires, the system reverts the status to “Friend”.
Use Case 2 – User Selected Intercessor Option
- The user could further designate one or more “Intercessors” for each and any of the affected contacts from among their common contacts.
- The role of “Intercessor”, should they accept it, would be to try to mediate and resolve the conflict among the affected parties within the stated period of the conflict.
- This is an option that the user may or may not exercise.
- The system will query the “Intercessors” through a possible multiplicity of communication channels if they consent to play that role (for conflict resolution and mediation).
- If the “Intercessor” agrees, the system will notify the user who had initially requested the help of the “Intercessor” as well as the affected “Conflict” contacts.
4. It is up to the “Intercessor” then to initiate the process of reconciliation.
Use case 3 – User elects to be “Intercessors”
Optionally, any user may elect to be an available as an
“Intercessor” in a social network or virtual community.
Use case 4 – System Recommends “Intercessors”
Optionally, the system recommends a list of available “Intercessors” to the user.
Use case 5 – “Conflict” contact looks for an “Intercessors”
Optionally, the system recommends a list of available “Intercessors” to a “Conflict” contact after the system has informed him of the change in his “Conflict” status by another user.
Description
This is envisioned as a software add-on to existing as well
as new social networking sites and virtual communities. The operation of the invention is described
in the above use cases.
Advantages
The chief advantage of this invention is that it enables one to retain one's "Friends" in social network even when one is cross with them. To wit, one announces publicly that he is cross with this or that person, others take note of it, and depending on the desire of both sides and their own inclinations could help them reconcile. So one does not necessarily wind up losing one's friend in a network due to a temporary emotional outburst or state of anger.
System Architecture & Design
The
architecturally significant components of the system are illustrated below:
Possible Modifications
Something analogous to this may be incorporated into email
clients – a “conflict” icon or button in which enables a user to designate one
or some of his/her contacts as “In Conflict” until further notice. The email server can then alert the person so
designated of the change in his/her status by another user. Optionally, all members of a person’s social
network could be advised of the status.
Furthermore, the email client can expire the status and revert it back
to “Friend” at a designated date and time (by the user). Likewise, the email client or the email
server could, for the duration, junk or otherwise archive email messages from
such users.
Saturday, March 8, 2025
Wednesday, March 5, 2025
Tuesday, March 4, 2025
Sunday, March 2, 2025
Automated Exploration of Knowledge with Large Language Models, Concept Extraction, and Cyc - A Proposal
In a previous post, I outlined how LLMs, Concept Extraction tools, and CYC could be combined to create a more structured,
semantically rich representation of the generated text.
In this post, I would like to build on that proposal and outline a way to automatically query LLMs based on their initial text in response to a user's question.
The process is as follows:
Overview of Steps:
- User poses a question to LLM
- LLM Generates Text
- Concept Extraction is applied to LLM Text
- CYC uses to those extracted concepts to find related concepts (please see below)
- Questions are created from those related concepts
- Those questions are posed to the LLM & Summarized
I have already discussed the substance of steps 1 to 3 in my previous post. Here I am going to outline steps 4 & 5.
Step 4:
The application of Concept Extraction tools to the LLM Text output results in a set of concepts. To generate more concepts starting from a set of initial
concepts and navigate to their "nearest" or related concepts, you can
follow these steps using Cyc's knowledge base. The goal is to explore
the relationships between concepts and expand the collection by finding the
most connected or related concepts to each starting concept.
Overview of Steps:
- Start
with a collection of initial concepts.
- For
each concept, find its nearest related concepts based on the
relationships in the Cyc knowledge base (e.g., “isa,” “part-of,”
“related-to”).
- Navigate
to these related concepts, recursively, if necessary, to explore
further connections.
- Generate
new concepts by expanding from the nearest neighbors.
- Visualization or Compilation
Key Queries in CycL for Navigating Concepts:
- Isa
(Is-A) Relationships: This tells you what class a concept belongs to.
It’s often used to explore a concept’s category.
- Part-Of:
This is useful to find what parts or components a concept belongs to.
- Related-To:
This can be used to find other concepts that are semantically related to a
given concept.
- Sub-collection:
This allows you to find all instances of a specific concept.
Example: Navigation from Concepts
Let's say you have a collection of starting concepts, such
as Cow, Milk, and Farm.
You can navigate the relationships by querying Cyc
for the nearest concepts based on different relationships. We’ll perform a
step-by-step expansion for each of these concepts, showing how to retrieve the
"nearest" related concepts.
Step A: Start with a Collection of Concepts
Let’s say you have the following concepts to begin with:
- Cow
- Milk
- Farm
These are the seed concepts from which you want to expand.
Step B: Define Queries to Explore Nearest Concepts
Now, you can create queries for each starting concept to
find the nearest related concepts. Here’s how you can perform some basic
queries in CycL for this task.
Query 1: Find the "Is-A" (Classification)
Relationships
This query identifies what category the concept
belongs to. For example, for Cow, you can check for its broader
classification.
(isa Cow ?X)
This asks, “What categories does Cow belong to?” and
will return categories such as Mammal, DomesticAnimal, etc.
Query 2: Find "Related-To" Concepts
Next, you can explore relationships with other
concepts. For example, you could ask:
(relatedTo Cow ?X)
This will give you concepts that are semantically related
to Cow, such as Milk, Udder, Farm, etc.
Query 3: Find "Part-Of" Relationships
The Part-Of relationship is useful for exploring components
or parts of a concept. For example:
(partOf Cow ?X)
This will return things that are part of a Cow, like
Udder or Hoof.
Step C: Process the Results and Expand
Once you have the results from these queries, you can expand
the set of concepts by finding the nearest related concepts from each of
the starting concepts.
Example Expansion (for the concept "Cow"):
- Starting
Concept: Cow
- Query:
isa Cow ?X
- Result:
Mammal, DomesticAnimal
- Query:
relatedTo Cow ?X
- Result:
Milk, Farm, Udder
- Query:
partOf Cow ?X
- Result:
Udder, Hoof
From Cow, the nearest related concepts are:
- Mammal
- DomesticAnimal
- Milk
- Farm
- Udder
- Hoof
- Starting
Concept: Milk
- Query:
isa Milk ?X
- Result:
SubstanceProducedByMammals
- Query:
relatedTo Milk ?X
- Result:
Dairy, Farm
- Query:
partOf Milk ?X
- Result:
Cow
From Milk, the nearest related concepts are:
- SubstanceProducedByMammals
- Dairy
- Farm
- Starting
Concept: Farm
- Query:
isa Farm ?X
- Result:
AgriculturalFacility
- Query:
relatedTo Farm ?X
- Result:
Milk, Cattle, DairyFarm
- Query:
partOf Farm ?X
- Result:
Field, Barn
From Farm, the nearest related concepts are:
- AgriculturalFacility
- Cattle
- DairyFarm
- Milk
- Field
- Barn
Step D: Recursively Expand to New Concepts
If you want to explore even further from each of the
nearest concepts, you can repeat the same queries for each new concept that was
found. For instance:
- If Milk
led you to Dairy, you can now run:
- (relatedTo
Dairy ?X)
This might give you related concepts like Cheese, Butter,
Yogurt, etc.
- Similarly,
if Cattle was found under Farm, you could run:
- (relatedTo
Cattle ?X)
And this might lead you to concepts like Beef, Livestock,
etc.
Step E: Visualization or Compilation
Once you’ve collected a large set of related concepts, you can organize or visualize them as a concept graph or network. This allows you to see how the concepts are connected, and explore new insights based on their relationships. It also enables a user to prune the concepts and to keep those that are germane to his interests.
Example Flow:
- Start
with: Cow
- Expand:
Mammal, DomesticAnimal, Milk, Farm, Udder, Hoof
- Next:
Explore Milk → Dairy, SubstanceProducedByMammals, Farm
- Next:
Explore Farm → AgriculturalFacility, DairyFarm, Cattle, Field, Barn
- Continue
expanding based on relations and parts...
Final Notes:
- Recursion:
You can use recursion to keep expanding from one set of related concepts
to the next. For each concept, you check its is-a, related-to,
and part-of relationships to find more concepts.
- Prioritization:
Depending on your task, you might prioritize certain relationships (e.g.,
“is-a” vs. “related-to”) to focus on more hierarchical or semantic links.
- Reasoning:
The ability to reason about relationships (e.g., if "A produces
B" and "B is needed for C", then "A may be needed for
C") helps enrich the exploration.
Step 5:
Next, we need to automatically create new questions to pose to the LLM. The simplest way is to begin with the "5W": "why,"
"when," "how," "what," "where," and
"who". We create new question by applying the "5Ws" to the newly discovered neartest-neighbor concepts. In order to do so meaningfully, we use the Cyc Query Language
(CQL).
CQL has the ability to identify which of the "5W" questions may be applicable to a concept by evaluating the nature of the concept and the
relationships it has within the Cyc knowledge base. Here’s how CQL could potentially help identify which
questions can apply:
- What:
This is generally applied to objects, entities, or types. If the concept
is a class or object, "what" could be used to query its
characteristics or define what it is. For instance, querying "What is
a dog?" or "What are the properties of a cat?"
- Where:
Applied when the concept involves a location, spatial information, or
events tied to geographical or spatial contexts. If the concept is related
to a place or location, "where" would likely apply. For
instance, "Where is the Eiffel Tower located?"
- Who:
Applied to concepts that refer to people, individuals, or specific agents.
If the concept represents a person or an actor in a particular event,
"who" would apply. Example: "Who is the president of the
United States?"
- When:
Used for temporal concepts, events, or instances that are related to time.
If the concept has a temporal dimension (such as an event or an
occurrence), then "when" would apply. Example: "When did
World War II start?"
- How:
Typically used when the concept is associated with processes, methods, or
causes. If the concept is a process, causal relationship, or method of
doing something, "how" would be applicable. Example: "How
does photosynthesis work?"
- Why:
Applied for causal explanations or reasons. This question type is most
often relevant to concepts related to reasons, causes, or justifications.
For instance, "Why do leaves turn yellow in the fall?"
To determine which of these questions can be applied, you would need to check the class or type of concept and its relationships in the knowledge base and then build a query accordingly. For example:
- What
could be queried with: (#$isa #$myConcept #$Thing)
- Where
could be queried with: (#$isa #$myConcept #$Location)
- Who
could be queried with: (#$isa #$myConcept #$Person)
- When
could be queried with: (#$isa #$myConcept #$Time)
- How
could be queried with: (#$isa #$Concept #$Process)
- Why
could be queried with: (#$cause #$myConcept ?Cause)
In this manner, we can programmatically create basic new textual questions to pose to LLMs (via API). We could also use "And" and "Or" to combinatorially compose those basic questions into new compound questions, which, we can then proceed to pose to the LLMs and generate new results.
One can programmatically invoke summarizer tool (e.g.: QuillBot, Grammarly, Ahrefs, Google Gemini, Vertex AI PaLM API, Document AI, TLDR This, Notta, Semrush, ChatGPT, ClickUp, Get Digest, Scribbr, Summary Generator, Paraphraser.io, Jasper, and Writesonic) to present a summary to the user.
Needless to say, there has to be user interface to any system that is built based on these ideas here to specify the depth and extent of the search for new concepts, to enable canned questions (in addition to the "5W") as well as user defined questions, calibration-like parameters for when to stop the search, how many combinatorial questions to generate, and a flexible architecture in which LLMs, Concept Extraction tools, and Summarize tools could be seamlessly swapped in and out.
I think one application of such an approach could be to explorations of Philosophy of Sciences, e.g., we start with an ontology from a subfield of science such as Physics, use the concepts from that ontology to generate new questions via CYC, and then pose those questions to the Corpus of Thomist and Neo-Thomist philosophy and see what we get.
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About Me
- Babak Makkinejad
- I had been a senior software developer working for HP and GM. I am interested in intelligent and scientific computing. I am passionate about computers as enablers for human imagination. The contents of this site are not in any way, shape, or form endorsed, approved, or otherwise authorized by HP, its subsidiaries, or its officers and shareholders.